{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3.1---Matplotlib简介以及图标窗口\n",
    "### Matplotlib---------一个Python版本的Matlab绘图接口，以2D为主，支持Python，numpy，pandas基本数据结构，运营高效且有较丰富的图表库"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 图表窗口1 ---> plt.show()\n",
    "\n",
    "plt.plot(np.random.rand(10))\n",
    "plt.show()\n",
    "# 直接生成图表"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1e7af0a7438>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 图表窗口2 ---> 魔法函数，嵌入图表\n",
    "\n",
    "% matplotlib inline\n",
    "x = np.random.randn(1000)\n",
    "y = np.random.randn(1000)\n",
    "plt.scatter(x,y)\n",
    "# 直接嵌入图表，不用plot.show()\n",
    "# <matplotlib.collections.PathCollection at ...>代表该图表对象"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<img src=\"\" width=\"720\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1e7af15d2e8>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 图表窗口3 ---> 魔法函数，弹出可交互的matplotlib窗口\n",
    "\n",
    "% matplotlib notebook\n",
    "s = pd.Series(np.random.randn(100))\n",
    "s.plot(style = 'k--o',figsize = (10,5))\n",
    "# 可交互的matplotlib窗口，不用plt.show()\n",
    "# 可以做一定的调整"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Warning: Cannot change to a different GUI toolkit: qt5. Using notebook instead.\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<img src=\"\" width=\"864\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x000001E7AEFE55C0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000001E7AF4F6A58>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 图表窗口4 ---> 魔法函数，弹出matplotlib控制台\n",
    "\n",
    "% matplotlib qt5\n",
    "df = pd.DataFrame(np.random.rand(50,2),columns = ['A','B'])\n",
    "df.hist(figsize = (12,5),color = 'g',alpha = 0.8)\n",
    "# 可交互性控制台\n",
    "# 如果已经设置了显示方式（比如notebook），需要重启然后再运行魔法函数\n",
    "# 网页嵌入的交互性窗口和控制台，两者只能显示一个\n",
    "\n",
    "# plt.close()\n",
    "# 关闭窗口\n",
    "\n",
    "# plt.gcf.clear()\n",
    "# 每次清空图表内内容"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3.2---图表的基本元素\n",
    "## 图表内基本参数设置"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<img src=\"\" width=\"432\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[Text(0,0,'0.00'),\n",
       " Text(0,0,'0.20'),\n",
       " Text(0,0,'0.40'),\n",
       " Text(0,0,'0.60'),\n",
       " Text(0,0,'0.80'),\n",
       " Text(0,0,'1.00'),\n",
       " Text(0,0,'1.20')]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 图名，图例，轴标签，轴边界，轴刻度，轴刻度标签等\n",
    "\n",
    "df = pd.DataFrame(np.random.rand(10,2),columns = ['A','B'])\n",
    "fig = df.plot(figsize = (6,4))\n",
    "# figsie: 创建图表窗口，设置窗口大小\n",
    "# 创建图表对象，并且赋值给fig\n",
    "\n",
    "plt.title('Interesting Graph - Check it out')    # 图名\n",
    "plt.xlabel('Plot Number')    # X轴标签\n",
    "plt.ylabel('Important var')  # Y轴标签\n",
    "\n",
    "plt.legend(loc = 'upper right')\n",
    "# 显示图例，loc表示相应的位置\n",
    "# 'best': 0, (only implemented for axes legends)(自适应方式)\n",
    "# 'upper right':1\n",
    "# 'upper left':2\n",
    "# 'low left':3\n",
    "# 'low right': 4\n",
    "# 'right':5\n",
    "# 'center left':6\n",
    "# 'center right':7\n",
    "# 'lower center':8\n",
    "# 'upper center':9\n",
    "# 'center':10\n",
    "\n",
    "plt.xlim([0,12])    # X轴边界\n",
    "plt.ylim([0,1.5])    # Y轴边界\n",
    "plt.xticks(range(10))  # 设置X的刻度\n",
    "plt.yticks([0,0.2,0.4,0.6,0.8,1.0,1.2])   # 设置Y的刻度\n",
    "fig.set_xticklabels('%.1f' %i for i in range(10))   # X轴刻度标签\n",
    "fig.set_yticklabels('%.2f' %i for i in [0,0.2,0.4,0.6,0.8,1.0,1.2])   # Y轴刻度标签\n",
    "# 范围只限定图表的长度，刻度则是决定显示的标尺 ---> 这里面的X轴的范围是0-12，但是刻度只是0-9，刻度标签使得其显示一位小数\n",
    "# 轴标签则是显示刻度的标签"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 其他元素可视性\n",
    "% matplotlib inline\n",
    "x = np.linspace(-np.pi,np.pi,num = 256,endpoint = True)\n",
    "c,s = np.cos(x),np.sin(x)\n",
    "plt.plot(x,c)\n",
    "plt.plot(x,s)\n",
    "# 通过ndarray来创建图表\n",
    "\n",
    "plt.grid(True,linestyle = \"--\",color = 'gray',linewidth = 0.5,axis = 'both')\n",
    "# 显示网格\n",
    "# linestyle：线型\n",
    "# color：颜色\n",
    "# linewidth：线宽（宽度）\n",
    "# axis：x,y，both：显示x/y/两个的格网\n",
    "plt.tick_params(bottom = True,top = False,left = True,right = False)\n",
    "# 刻度显示\n",
    "\n",
    "import matplotlib\n",
    "matplotlib.rcParams['xtick.direction'] = 'out'\n",
    "matplotlib.rcParams['ytick.direction'] = 'inout'\n",
    "# 设置刻度的方向：in , out , inout\n",
    "# 这里需要导入matplotlib，而不仅仅是导入matplotlib.pyplot\n",
    "\n",
    "\n",
    "frame = plt.gca()\n",
    "# plt.axis('off')\n",
    "# 关闭坐标轴\n",
    "# frame.axes.get_xaxis().set_visible(False)\n",
    "# frame.axes.get_yaxis().set_visible(False)\n",
    "# x/y轴不可见"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3.3---图表的样式参数\n",
    "## linestyle, style, color, marker"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1e7b0a7bef0>]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# linestyle参数\n",
    "\n",
    "plt.plot([i**2 for i in range(100)],\n",
    "        linestyle = '-.')\n",
    "\n",
    "# '-':           solid line style\n",
    "# '--':         dashed line style\n",
    "# '-.':         dash-dot line style\n",
    "# ':':          dotted line style"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1e7b0ab0940>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# marker 参数\n",
    "\n",
    "s = pd.Series(np.random.randn(100).cumsum())\n",
    "s.plot(linestyle = '--',\n",
    "      marker = '.')\n",
    "\n",
    "# '.':                           point marker\n",
    "# ',':                           pixel marker\n",
    "# 'o':                          circle marker\n",
    "# 'v':                          triangle_down marker\n",
    "# '^':                          triangle_up marker\n",
    "# '<':                          triangle_left marker\n",
    "# '>':                          triangle_right marker\n",
    "# '1':                          tri_down marker\n",
    "# '2':                          tri_up marker\n",
    "# '3':                          tri_left marker\n",
    "# '4':                          tri_right marker\n",
    "# 's':                          square marker\n",
    "# 'p':                          pentagon marker\n",
    "# '*':                          star marker\n",
    "# 'h':                          hexagonl marker\n",
    "# 'H':                          hexagon2 marker\n",
    "# '+':                          plus marker\n",
    "# 'x':                          x marker\n",
    "# 'D':                          diamond marker\n",
    "# 'd':                          thin diamond marker\n",
    "# '/':                          vline marker\n",
    "# '_':                          hline marker"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1e7b0b4dcc0>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# color参数\n",
    "\n",
    "plt.hist(np.random.randn(100),\n",
    "        color = 'g',alpha = 0.8)\n",
    "\n",
    "# alpha: 0-1,透明度\n",
    "# 常用颜色简写：red-r, green-g, black-k, blue-b, yellow-y\n",
    "\n",
    "\n",
    "df = pd.DataFrame(np.random.randn(1000,4),columns = list('ABCD'))\n",
    "df = df.cumsum()\n",
    "df.plot(style = '--',alpha = 0.8,colormap = 'GnBu')\n",
    "\n",
    "# colormap: 颜色板，包括：\n",
    "# Accent, Accent_r, Blues, Blues_r, BrBG, BrBG_r, BuGn, BuGn_r, BuPu, BuPu_r, ...\n",
    "# 其他参数见“颜色参数.docx”\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "ename": "NonGuiException",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNonGuiException\u001b[0m                           Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-12-45ae8286b2a5>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mts\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mSeries\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrandn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1000\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcumsum\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindex\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdate_range\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'2018-1-1'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mperiods\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m1000\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m \u001b[0mts\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstyle\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'--g.'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mgrid\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      5\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      6\u001b[0m \u001b[1;31m# style ---> 风格字符串，这里包含了linestyle('--'), marker('.'), color('g')\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32md:\\python\\lib\\site-packages\\pandas\\plotting\\_core.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, kind, ax, figsize, use_index, title, grid, legend, style, logx, logy, loglog, xticks, yticks, xlim, ylim, rot, fontsize, colormap, table, yerr, xerr, label, secondary_y, **kwds)\u001b[0m\n\u001b[0;32m   2501\u001b[0m                            \u001b[0mcolormap\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcolormap\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtable\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtable\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0myerr\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0myerr\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2502\u001b[0m                            \u001b[0mxerr\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mxerr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlabel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msecondary_y\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msecondary_y\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2503\u001b[1;33m                            **kwds)\n\u001b[0m\u001b[0;32m   2504\u001b[0m     \u001b[0m__call__\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__doc__\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mplot_series\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__doc__\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2505\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32md:\\python\\lib\\site-packages\\pandas\\plotting\\_core.py\u001b[0m in \u001b[0;36mplot_series\u001b[1;34m(data, kind, ax, figsize, use_index, title, grid, legend, style, logx, logy, loglog, xticks, yticks, xlim, ylim, rot, fontsize, colormap, table, yerr, xerr, label, secondary_y, **kwds)\u001b[0m\n\u001b[0;32m   1925\u001b[0m                  \u001b[0myerr\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0myerr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mxerr\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mxerr\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1926\u001b[0m                  \u001b[0mlabel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlabel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msecondary_y\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msecondary_y\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1927\u001b[1;33m                  **kwds)\n\u001b[0m\u001b[0;32m   1928\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1929\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32md:\\python\\lib\\site-packages\\pandas\\plotting\\_core.py\u001b[0m in \u001b[0;36m_plot\u001b[1;34m(data, x, y, subplots, ax, kind, **kwds)\u001b[0m\n\u001b[0;32m   1727\u001b[0m         \u001b[0mplot_obj\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mklass\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msubplots\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msubplots\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkind\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mkind\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1728\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1729\u001b[1;33m     \u001b[0mplot_obj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgenerate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1730\u001b[0m     \u001b[0mplot_obj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1731\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mplot_obj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mresult\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32md:\\python\\lib\\site-packages\\pandas\\plotting\\_core.py\u001b[0m in \u001b[0;36mgenerate\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    250\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_compute_plot_data\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    251\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_setup_subplots\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 252\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_make_plot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    253\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_add_table\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    254\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_make_legend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32md:\\python\\lib\\site-packages\\pandas\\plotting\\_core.py\u001b[0m in \u001b[0;36m_make_plot\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    975\u001b[0m                              \u001b[0mstacking_id\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mstacking_id\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    976\u001b[0m                              \u001b[0mis_errorbar\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mis_errorbar\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 977\u001b[1;33m                              **kwds)\n\u001b[0m\u001b[0;32m    978\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_add_legend_handle\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnewlines\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    979\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32md:\\python\\lib\\site-packages\\pandas\\plotting\\_core.py\u001b[0m in \u001b[0;36m_ts_plot\u001b[1;34m(cls, ax, x, data, style, **kwds)\u001b[0m\n\u001b[0;32m   1016\u001b[0m         \u001b[0mlines\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcls\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_plot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstyle\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mstyle\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1017\u001b[0m         \u001b[1;31m# set date formatter, locators and rescale limits\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1018\u001b[1;33m         \u001b[0mformat_dateaxis\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfreq\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1019\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mlines\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1020\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32md:\\python\\lib\\site-packages\\pandas\\plotting\\_timeseries.py\u001b[0m in \u001b[0;36mformat_dateaxis\u001b[1;34m(subplot, freq, index)\u001b[0m\n\u001b[0;32m    342\u001b[0m         \u001b[1;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'index type not supported'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    343\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 344\u001b[1;33m     \u001b[0mpylab\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdraw_if_interactive\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32md:\\python\\lib\\site-packages\\IPython\\utils\\decorators.py\u001b[0m in \u001b[0;36mwrapper\u001b[1;34m(*args, **kw)\u001b[0m\n\u001b[0;32m     41\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     42\u001b[0m         \u001b[0mwrapper\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcalled\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 43\u001b[1;33m         \u001b[0mout\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     44\u001b[0m         \u001b[0mwrapper\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcalled\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     45\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mout\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32md:\\python\\lib\\site-packages\\matplotlib\\backend_bases.py\u001b[0m in \u001b[0;36mdraw_if_interactive\u001b[1;34m(cls)\u001b[0m\n\u001b[0;32m    174\u001b[0m             \u001b[0mmanager\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mGcf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_active\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    175\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mmanager\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 176\u001b[1;33m                 \u001b[0mcls\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtrigger_manager_draw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmanager\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    177\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    178\u001b[0m     \u001b[1;33m@\u001b[0m\u001b[0mclassmethod\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32md:\\python\\lib\\site-packages\\matplotlib\\backends\\backend_nbagg.py\u001b[0m in \u001b[0;36mtrigger_manager_draw\u001b[1;34m(manager)\u001b[0m\n\u001b[0;32m    244\u001b[0m     \u001b[1;33m@\u001b[0m\u001b[0mstaticmethod\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    245\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mtrigger_manager_draw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmanager\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 246\u001b[1;33m         \u001b[0mmanager\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    247\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    248\u001b[0m     \u001b[1;33m@\u001b[0m\u001b[0mstaticmethod\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32md:\\python\\lib\\site-packages\\matplotlib\\backend_bases.py\u001b[0m in \u001b[0;36mshow\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m   2685\u001b[0m         \u001b[0moptional\u001b[0m \u001b[0mwarning\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2686\u001b[0m         \"\"\"\n\u001b[1;32m-> 2687\u001b[1;33m         \u001b[1;32mraise\u001b[0m \u001b[0mNonGuiException\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2688\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2689\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mdestroy\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNonGuiException\u001b[0m: "
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# style参数: 可以包含linestyle,marker,color\n",
    "\n",
    "ts = pd.Series(np.random.randn(1000).cumsum(), index = pd.date_range('2018-1-1',periods = 1000))\n",
    "ts.plot(style = '--g.', grid = True)\n",
    "\n",
    "# style ---> 风格字符串，这里包含了linestyle('--'), marker('.'), color('g')\n",
    "# plot内也有grid参数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['bmh', 'classic', 'dark_background', 'fast', 'fivethirtyeight', 'ggplot', 'grayscale', 'seaborn-bright', 'seaborn-colorblind', 'seaborn-dark-palette', 'seaborn-dark', 'seaborn-darkgrid', 'seaborn-deep', 'seaborn-muted', 'seaborn-notebook', 'seaborn-paper', 'seaborn-pastel', 'seaborn-poster', 'seaborn-talk', 'seaborn-ticks', 'seaborn-white', 'seaborn-whitegrid', 'seaborn', 'Solarize_Light2', 'tableau-colorblind10', '_classic_test']\n"
     ]
    },
    {
     "ename": "NonGuiException",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNonGuiException\u001b[0m                           Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-13-491280aa5898>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      8\u001b[0m \u001b[0mpsl\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0muse\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'tableau-colorblind10'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      9\u001b[0m \u001b[0mts\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mSeries\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrandn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1000\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcumsum\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mindex\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdate_range\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'2018-1-1'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mperiods\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m1000\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 10\u001b[1;33m \u001b[0mts\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstyle\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'--g.'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mgrid\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mfigsize\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;36m10\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m6\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     11\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     12\u001b[0m \u001b[1;31m# 一旦选用样式后，所有图标都会有样式，重启之后才能够关掉\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32md:\\python\\lib\\site-packages\\pandas\\plotting\\_core.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, kind, ax, figsize, use_index, title, grid, legend, style, logx, logy, loglog, xticks, yticks, xlim, ylim, rot, fontsize, colormap, table, yerr, xerr, label, secondary_y, **kwds)\u001b[0m\n\u001b[0;32m   2501\u001b[0m                            \u001b[0mcolormap\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcolormap\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtable\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtable\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0myerr\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0myerr\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2502\u001b[0m                            \u001b[0mxerr\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mxerr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlabel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msecondary_y\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msecondary_y\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2503\u001b[1;33m                            **kwds)\n\u001b[0m\u001b[0;32m   2504\u001b[0m     \u001b[0m__call__\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__doc__\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mplot_series\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__doc__\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2505\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32md:\\python\\lib\\site-packages\\pandas\\plotting\\_core.py\u001b[0m in \u001b[0;36mplot_series\u001b[1;34m(data, kind, ax, figsize, use_index, title, grid, legend, style, logx, logy, loglog, xticks, yticks, xlim, ylim, rot, fontsize, colormap, table, yerr, xerr, label, secondary_y, **kwds)\u001b[0m\n\u001b[0;32m   1925\u001b[0m                  \u001b[0myerr\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0myerr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mxerr\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mxerr\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1926\u001b[0m                  \u001b[0mlabel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlabel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msecondary_y\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msecondary_y\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1927\u001b[1;33m                  **kwds)\n\u001b[0m\u001b[0;32m   1928\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1929\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32md:\\python\\lib\\site-packages\\pandas\\plotting\\_core.py\u001b[0m in \u001b[0;36m_plot\u001b[1;34m(data, x, y, subplots, ax, kind, **kwds)\u001b[0m\n\u001b[0;32m   1727\u001b[0m         \u001b[0mplot_obj\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mklass\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msubplots\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msubplots\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkind\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mkind\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1728\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1729\u001b[1;33m     \u001b[0mplot_obj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgenerate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1730\u001b[0m     \u001b[0mplot_obj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1731\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mplot_obj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mresult\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32md:\\python\\lib\\site-packages\\pandas\\plotting\\_core.py\u001b[0m in \u001b[0;36mgenerate\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    250\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_compute_plot_data\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    251\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_setup_subplots\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 252\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_make_plot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    253\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_add_table\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    254\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_make_legend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32md:\\python\\lib\\site-packages\\pandas\\plotting\\_core.py\u001b[0m in \u001b[0;36m_make_plot\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    975\u001b[0m                              \u001b[0mstacking_id\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mstacking_id\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    976\u001b[0m                              \u001b[0mis_errorbar\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mis_errorbar\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 977\u001b[1;33m                              **kwds)\n\u001b[0m\u001b[0;32m    978\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_add_legend_handle\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnewlines\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    979\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32md:\\python\\lib\\site-packages\\pandas\\plotting\\_core.py\u001b[0m in \u001b[0;36m_ts_plot\u001b[1;34m(cls, ax, x, data, style, **kwds)\u001b[0m\n\u001b[0;32m   1016\u001b[0m         \u001b[0mlines\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcls\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_plot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstyle\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mstyle\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1017\u001b[0m         \u001b[1;31m# set date formatter, locators and rescale limits\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1018\u001b[1;33m         \u001b[0mformat_dateaxis\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfreq\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1019\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mlines\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1020\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32md:\\python\\lib\\site-packages\\pandas\\plotting\\_timeseries.py\u001b[0m in \u001b[0;36mformat_dateaxis\u001b[1;34m(subplot, freq, index)\u001b[0m\n\u001b[0;32m    342\u001b[0m         \u001b[1;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'index type not supported'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    343\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 344\u001b[1;33m     \u001b[0mpylab\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdraw_if_interactive\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32md:\\python\\lib\\site-packages\\IPython\\utils\\decorators.py\u001b[0m in \u001b[0;36mwrapper\u001b[1;34m(*args, **kw)\u001b[0m\n\u001b[0;32m     41\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     42\u001b[0m         \u001b[0mwrapper\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcalled\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 43\u001b[1;33m         \u001b[0mout\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     44\u001b[0m         \u001b[0mwrapper\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcalled\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     45\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mout\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32md:\\python\\lib\\site-packages\\matplotlib\\backend_bases.py\u001b[0m in \u001b[0;36mdraw_if_interactive\u001b[1;34m(cls)\u001b[0m\n\u001b[0;32m    174\u001b[0m             \u001b[0mmanager\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mGcf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_active\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    175\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mmanager\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 176\u001b[1;33m                 \u001b[0mcls\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtrigger_manager_draw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmanager\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    177\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    178\u001b[0m     \u001b[1;33m@\u001b[0m\u001b[0mclassmethod\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32md:\\python\\lib\\site-packages\\matplotlib\\backends\\backend_nbagg.py\u001b[0m in \u001b[0;36mtrigger_manager_draw\u001b[1;34m(manager)\u001b[0m\n\u001b[0;32m    244\u001b[0m     \u001b[1;33m@\u001b[0m\u001b[0mstaticmethod\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    245\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mtrigger_manager_draw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmanager\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 246\u001b[1;33m         \u001b[0mmanager\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    247\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    248\u001b[0m     \u001b[1;33m@\u001b[0m\u001b[0mstaticmethod\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32md:\\python\\lib\\site-packages\\matplotlib\\backend_bases.py\u001b[0m in \u001b[0;36mshow\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m   2685\u001b[0m         \u001b[0moptional\u001b[0m \u001b[0mwarning\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2686\u001b[0m         \"\"\"\n\u001b[1;32m-> 2687\u001b[1;33m         \u001b[1;32mraise\u001b[0m \u001b[0mNonGuiException\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2688\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2689\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mdestroy\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNonGuiException\u001b[0m: "
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 整体风格样式\n",
    "\n",
    "import matplotlib.style as psl\n",
    "print(plt.style.available)\n",
    "\n",
    "# 查看样式列表\n",
    "\n",
    "psl.use('tableau-colorblind10')\n",
    "ts = pd.Series(np.random.randn(1000).cumsum(),index = pd.date_range('2018-1-1',periods = 1000))\n",
    "ts.plot(style = '--g.',grid = True,figsize = (10,6))\n",
    "\n",
    "# 一旦选用样式后，所有图标都会有样式，重启之后才能够关掉"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3.4---刻度，注解，图表输出\n",
    "## 主刻度，次刻度"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 刻度\n",
    "\n",
    "from matplotlib.ticker import MultipleLocator, FormatStrFormatter\n",
    "t = np.arange(0.0, 100.0, 1)\n",
    "s = np.sin(0.1 * np.pi * t) * np.exp(-t * 0.01)\n",
    "ax = plt.subplot(111)            # 注意：一般都在ax中设置，不在plot中进行设置\n",
    "plt.plot(t,s,'--*')\n",
    "plt.grid(True, linestyle = '--',color = 'gray',linewidth = 0.5,axis = 'both')\n",
    "# 网格\n",
    "# plt.legend()   图例\n",
    "\n",
    "xmajorLocator = MultipleLocator(10)     # 将x的主刻度标签设置为10的倍数\n",
    "xmajorFormatter = FormatStrFormatter('%.0f')          # 设置x轴标签文本的格式\n",
    "xminorLocator = MultipleLocator(5)     # 将x的次刻度标签设置为5的倍数\n",
    "ymajorLocator = MultipleLocator(0.5)   # 将y的主刻度标签设置为0.5的倍数\n",
    "ymajorFormatter = FormatStrFormatter('%.1f')         # 设置y轴标签文本的格式\n",
    "yminorLocator = MultipleLocator(0.1)  # 将y的主刻度标签设置为0.1的倍数\n",
    "\n",
    "ax.xaxis.set_major_locator(xmajorLocator)               # 设置x轴的主刻度\n",
    "ax.xaxis.set_major_formatter(xmajorFormatter)           # 设置x轴的标签文本格式\n",
    "ax.xaxis.set_minor_locator(xminorLocator)              # 设置x轴的次刻度\n",
    "\n",
    "ax.yaxis.set_major_locator(ymajorLocator)               # 设置y轴的主刻度\n",
    "ax.yaxis.set_major_formatter(ymajorFormatter)           # 设置y轴的标签文本格式\n",
    "ax.yaxis.set_minor_locator(yminorLocator)              # 设置y轴的次刻度\n",
    "\n",
    "ax.xaxis.grid(True,which = 'both')               # x坐标轴的网格使用主刻度\n",
    "ax.yaxis.grid(True,which = 'minor')             # y坐标轴的网格使用次刻度\n",
    "# which：网格显示\n",
    "\n",
    "# 删除坐标轴的刻度显示\n",
    "\n",
    "# ax.yaxis.set_major_locator(plt.NullLocator())           \n",
    "# ax.xaxis.set_major_formatter(plt.NullFormatter())           "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(5,0.5,'hahaha')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 注解\n",
    "\n",
    "df = pd.DataFrame(np.random.randn(10,2))\n",
    "df.plot(style = '--o')\n",
    "plt.text(5,0.5,'hahaha',fontsize = 10)\n",
    "# 注解：---> 横坐标，纵坐标，注解字符串"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 图表输出\n",
    "\n",
    "df = pd.DataFrame(np.random.randn(1000,4),columns = list('ABCD'))\n",
    "df = df.cumsum()\n",
    "df.plot(style = '--.',alpha = 0.5)\n",
    "plt.legend(loc = 'upper left')\n",
    "plt.savefig('D:\\Python练习\\picture.png',\n",
    "           dpi = 400,\n",
    "           bbox_inches = 'tight',\n",
    "           facecolor = 'g',\n",
    "           edgecolor = 'b'\n",
    "           )\n",
    "# 可支持 png, pdf, svg, ps, eps ...等，可以以后缀名来指定\n",
    "# dpi是分辨率\n",
    "# bbox_inches: 图表需要保存的部分。如果设置为'tight',则会尝试剪除图表周围的空白部分。\n",
    "# facecolor，edgecolor：图像的背景色，默认为 'w'(白色)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3.5---子图\n",
    "## 在matplotlib中，整个图像为一个Figure对象\n",
    "## 在Figure对象中，可以包含一个或者多个Axes对象\n",
    "## 每个Axes(ax)对象都是一个拥有自己坐标系统的绘图区域\n",
    "\n",
    "### plt.figure ,   plt.subplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1e7b10421d0>]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x144 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 288x144 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plt.figure()     绘图对象\n",
    "# plt.figure(num = None, figsize = None, dpi = None, facecolor = None, edgecolor = None,\n",
    "#           frameon = True, FigureClass = <class 'matplotlib.figure.Figure'>, **kargs )\n",
    "\n",
    "fig1 = plt.figure(num = 1,figsize = (4,2))\n",
    "plt.plot(np.random.rand(50).cumsum(),'k--')\n",
    "fig2 = plt.figure(num = 2,figsize = (4,2))\n",
    "plt.plot(50-np.random.rand(50).cumsum(),'k--')\n",
    "# num: 图表序号，可以试一试不写或者都为同一个数字的情况，图表应该会如何显示\n",
    "# figsize：图表大小\n",
    "\n",
    "# 当我们调用plot时，如果设置plt.figure(), 则会自动调用figure()S生成一个figure，严格地讲，是生成subplots(111)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1e7b0fc3cf8>,\n",
       " <matplotlib.lines.Line2D at 0x1e7b0fc3710>,\n",
       " <matplotlib.lines.Line2D at 0x1e7b0fc3390>,\n",
       " <matplotlib.lines.Line2D at 0x1e7b0fc3908>]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ll3m4UhEREd/QpqvzwsLCqKqqIjw8nKqqKkJDQ0/7uMzMTDIzM1m1ahVZWVnnVai0nsvlav73xIkT2bRpEzNnzuQPf/gDvXr1ar4vJSWFX/7yl54oUURExGe1KURlZGSwbds2xo0bx7Zt28jIyHB3XdICn3/+Of369SMyMpJ169bx4osvUlxcTGFhIYWFhRQVFZGXl0dKSgqPPvooYWFhjBs3ztNli4iIdArnDFHLli1j37591NbW8sQTTzBhwgQuueQSXn/9dbZu3UpkZCSzZs3qiFrlW9u2beORRx7h3Xff5dNPP+Wiiy6ioKCA9evXExcXR7du3Rg6dCjdunVrbklw1VVXebhqERGRzuWcIWrmzJmnvf3mm292ezFydrt37+bRRx/l1VdfJSoqiv/+7/9u3ndu9uzZzJ4928MVioiIdB3qWO4jHA4HkyZNoqioiF/84hc8+OCD2ndORETEgxSivJTL5eKDDz7g+eef58UXXyQgIICXX36Z3r170717d0+XJyIi0uUpRHmRI0eOsHTpUnbs2MHGjRvZvn073bp1IycnhyFDhjB27FhPlygiIiLfatc+UXJmdXV1vPnmm8ybN49Vq1YBUFRUxL333tu85um5555j//79DBkyxMPVioiIyHdpJKqDrV27lmeffZY1a9ZQW1tLVFQUo0aNAmDgwIEcOXKEhIQELBaLhysVERGRs1GI6kANDQ3ceeed1NXVMW/ePL73ve9x2WWX4e/vD4Cfnx/dunXzcJUiXYfT6eTZZ58lPDxcV7eKSKspRLWzuro6nnrqKe68805CQkJYs2YNPXv2bO7fJCKe8+mnnxIXF4fdbvd0KSLig7Qmqp04HA6WLFnCoEGDePDBB5s3++3bt68ClIgXqKioIDc3l+HDh3u6FBHxUQpRbuZyuXjttdcYMmQIs2fPJiwsjA8++IDvf//7ni5NRE6wZs0aJk2apPWHItJmms47Ty6Xi9LSUg4cOMDQoUOx2WwsXLgQi8XC0qVLue6667BalVVFvElOTg6hoaEkJSWxd+/e0z4mKyuLLVu2UFFRQWZmJvPnz2f+/PkdXOnJFrzzVfsde8rQdju2dKz2fJ3IyRSi2iArK4s//elPbN26lQMHDlBbWwtAfn4+KSkpvPbaa8TExGCz2TxcqYicTn5+Pjk5OeTm5tLU1ITdbmf58uVcd911zY/JzMwkMzOTVatWkZWV5cFqRcRbKUS1gMvl4vPPPyc5OZnk5GQOHjzI2rVrGT9+PFdddRU9evSgR48ezduwxMfHe7hiETmbiRMnMnHiRAD27t3LJ598clKAEhFpCYWocygsLOS2227jnXfe4bHHHmPBggVcc801HD58WAvERUREujCFqLN45513uOWWW6isrOSPf/wjt99+O2D6OYlI55Cenk56erqnyxARH6Q0cAZ///vfueuuuxg6dCjr1q1j0KBBni5JREREvIhC1BlMnTqVgwcP8thjjxEYGOjpckRERMTL6Nr7E2zYsIG5c+fS1NRESkoKjz/+uAKUiIiInJZC1LeWLFnCxIkT2bx5M0ePHvV0OSIiIuLlunyIcrlc/Pa3v2X27NmMHj2aTZs20b17d0+XJSIiIl6uS4Wouro6NmzYwGuvvUZ1dTUATz31FI8++ihz5szhvffeIyYmxsNVioiIiC/oMgvL9+3bx5QpU8jOzgZg69atDBs2jPj4eH73u9/x8MMPaw8tERERabEuEaKysrK45pprqK+vZ8mSJQwdOpQ+ffoAcMMNN3i4OhEREfFFnT5EuVwufv7znxMcHMy6desYMGCAp0sSERGRTqBThyi73U5gYCCvvPIKTqeTbt26ebokERER6SQ65cLyyspK5s6dyzXXXENjYyPx8fEKUCIiIuJWnS5Ebd68mQsvvJDFixczduxYrNZO9xRFRETEC3SahOFwOPjd737H2LFjcTgcfPzxxyxYsACbzebp0kRERKQT8ukQ1djYSE5ODgDV1dUsXLiQWbNmsW3bNsaOHevh6jqWywWNjVBZCbt3g8Nhbl+/Hp5+GhoaPFufiIhIZ+NzIcrhcPD8889z5ZVXEh0dzahRo2hqaiIyMpKsrCyWLFlCVFSUp8vsMAsXQnAw2GwQEACRkdC3Lxw6ZO7/97/hzjth7FjIzfVsrSIiIp2JT12d98UXX3Drrbfy5ZdfkpGRwbx58xg/fjxOpxOAhIQED1fYcex2CAyEwYPh7rvNvwMDISgI4uNNmAJ48EHo3x/+8z9h+HD4+9/hBz/wbO0iIiKdgU+EKJfL9W038WAqK5t45ZWl3HDDzC7bYTw3Fy67DF56CSZONKNMZ+LvDzNmwMiRMGcOzJ1rRq1uuqnj6hUREemMvHY6z+VysXbtFoYOXcsPfnAbAE89NYC9e7dz442zuPJKC7W1Hi7SA5xOuPVWqKuDgQNb/n2pqfDhh/DMMzBzprnt5pshNhZ694bRo+Hjj9unZhERkc7I60aiSkpKePnll/nzn7ewd+/jwGB69txGY2Mjs2b5068fFBXBk0/CDTfAG2+Y0Zau4m9/g40b4cUXISmpdd9rs8Eddxz/etIkCA+H8nKzAH3ePMjONlOCIiIicnbnFaJyc3NZs2YNTqeT4cOHM27cuPMu6Kmnnuaxx6qB50lIqGHZskbGjXsIgCuvNH8A+vQxC6Y/+OD4badTXGzWCHnCV1/BH/8I994LmZnnf7w9e+Dhh+Gqq8y03PmaM8f8AfNznDEDvvwSLrro/I8tIiLS2bV5Os/pdPLuu+8ye/Zs7rrrLrZv305RUdF5F3TgwE+A33PDDX7k5kYyblzYaR/3ox+ZkPLdAJWXB3fdZS7pr6qCIUNg+nRz2X9Hqq2F66+Hl1+GUaPgllvg8OHzO+Ybb4CfHzz7LLh7OdgVV8CBAwpQIiIiLdXmEHXo0CFiYmKIiYnBz8+PwYMHN/dsOh/33RfC00/DK69ARMTZHztkiPn7ww/hD3+A3/4WBg0yC66//NJM8917L/zrX2b90M9+ZvoodYT6enNV3BtvmCvkliwxQQqgqQlKSkyo2rfPLBRvbDzzsY71eHrwQdixA1JS2qfmyEiz5mr9+vY5voiISGfS5hBVWVlJxAkpJyIigko3JJTBg+GHP2zdSMvixfDzn8Ojj8K118LOneZqtKAgM/2VmwuzZ5uptQsuMGuq2ltMDKxcCd/7ngl42dlmHReY0BcXZ9Y0padDv37m708/PfkYTqcZyerdGz77zNzW2nVQrbVwIYwfDxs2tO95REREfJ1bF5Z/t+VAVlYWW7ZsoaKigszMTObPn8/8+fPdeUrAXHGWmgoXXwz/8R+n3p+YCC+8ALfdBsuXmyvS2ktpqQmBf/iDCUbH9O59/N/9+8Of/2z6OgUEmLD0xhsmTIEJTIWF8PjjJlhlZnbc4vmbb4b//m+45x7IyjKL0UU6o4qKClasWEF1dTUWi4URI0YwevRoT5clIj6kzSHquyNPlZWVhIeHn/SYzMxMMjMzWbVqFVlZWW2v8hz8/WHBgnM/7pJLzB+AgwfNlWnHmlK6y113wYoVZmTsxBB1oh49TEg50W23Hf/3/ffDJ59A9+7mKrwf/AA6ah/lkBD4n/8xVz7+858nX80n0plYrVYmT55MUlISdrudhQsX0qtXry7VtFdEzk+bP5qTkpIoKSmhrKyMpqYmtm/fTkZGhjtrazcNDTBhgpn6c2evqVdegVdfNYFu+PC2H+eNN2DpUti1y4wMdVSAOmbWLNPM8yc/MYv3RTqj8PBwkr6dHw8MDCQ+Pp6qqioPVyUivqTNH882m42rr76aRYsW8dRTTzFo0CCf+Q0uIAB+8xvTb2nmTPdszrtjh2m5MHo0PPTQ+R2rWzcTZL4zsNdhLBYzRdq3LxzbhvCVV8yI2KpVZrNjkc6krKyMw4cPk5yc7OlSRMSHnNeaqH79+tHv2EIeH/P975sr9e64w6xXuuEGM43VVg89ZNY4LV5s2hD4uv79Ydu2418XF8OaNWah+3XXmT34fCQzi5yV3W5n6dKlXHnllQSd0Gm2o9Z0eosF72jYWaS1OsHHfdvNn28acb78Mhw9evz2W2+FadNg6tSWXyW4aBEUFECvXu1Tq6fdc48Zafuf/4HHHjNtEJ57zvyMWsvhgC++MOvSmprMn3Hj2q91g8iZOBwOli5dypAhQxj4nX2UOmpNp4j4Lq/dO6+jfO975oq9F180X9fUmD3kpk83U3Pvv3/m6avKStO7qbbWLFAfMKDDyvYIPz+zYP6LL8yieaez5d/rcJi/s7JMcB01ynRIv/56sxnysc+o1hxT5Hy4XC7efPNN4uLiGDNmjKfLEREf1OVD1HeFhpo+U//8Jxw5ApMnw+WXw/795v76evNBX1dnRmH+939hyxbP1tzRBg2CTZtM0AR4+22z/97puFxmgfrvf2++zsgw37dkCWzdCtu3m5/3xIkmSGVkwDffdMzzkK7twIEDfPXVV+zdu5enn36ap59+ml27dnm6LBHxIV16Ou9M/PxMy4E5c8wWK889Z5pjgpnWeuEFE7YqK81UoBu2DPQ5x/pHFRSYxfkXXWTWTAUHH3+My2WanT75pJkCBLNY/vnnT3/MtDSz9urBB+Hdd9u1fBF69uzJgpb0RhEROQONRJ1FYCDcfbcZMQkNNbdde635kJ85E157zUxFdWVJSfB//2c6nN900/FpOzDb8PzhD2bx/rEQdTZxcfDLX8Lq1WYaVURExJspRLXAiYvLr73WdBL/5z9NGwIxVzb++c9mm5sf/ciMQP3pT/Bf/2XaIvz97y1foH/33Wa91U9/enIg83bV1eZ10dTk6UpERKSjKESJW9x9N/ziF/CPf5hRJJvNhMznn29ds9DAQLN+6uuvTSjzZk1NcN99Zl3XmjXwyCPmj4iIdA1aEyVu89vfmk7nkyebP/fc07qNpI851mj0yivdX6O71NWZXmNvvWW28fnJT8yeif/v/5n1YTNmeLpCERFpbxqJErexWEx4OvHrth7nqqvM3+7oJu9u5eVmo+tVq+CvfzUBCsyVmqNGwbx5kJPj0RJFRKQDKESJ13rrLYiOhilTTJPPLVs6Zp3Usam5vLxT79u922yH8+mnpk3Dj398/L7AQHj9dbOtUGdqbO10muaq6uElInIyhSjxWqNHw9y5Jrg8+CBkZkJsrNmg2d1cLrN5dH09bN5s1mX17g2TJpkrBp94wjyuVy+z7c369WY677t69IA33zRXLHqbqirz/ABKSqCw8OyPP3DA7DHZpw+MHw8fftjuJYqI+BSFKPFaCQnw9NNmauzQITPy8/DDx5t8utO//gU33ggvvWTaMezbB7/+NeTmwu9+Z4IVmEXyzzwDF1985mONHWt6XjmdsHat+2tti9JS09B0zpzjV0/26GGunty8Gfbsga9O2DrthhvMc/iv/zLBcckS87xEROQ4hSjxCUlJJuQ89JAJMvv3m4Xsp9uSJyfHBKGDB1t2bJfLXFnYsyfcfLO5LTUVHn3UhIvSUli6tPU1v/CCWRz/97+3/nvdqbDQjCRt22ZCk8ViRvjuuMOMmo0ebUab5s07/j0BAeb57917PGCe2EhVRER0dZ74qMWLzYd8ZaVp6HlsEfuGDWabnqYm07vqz382geFsi9xXrjRbzjz/vFnXdCKbzazLaoubbzYh5cc/No1Er7++bcc5HwcOmBGoQ4fgnXfMv8Fsr/OXv5ggunz58duOWbSo42sVEfE1ClHikx5+2ASDP/7RjJDceCP0729GVX7+c7jmGrOOat480wH91VdPfxyHw6x5ysgwozTu5Odnzjt5splGi4k5HmI6gstlpj6LikzvrtPtsRsRAbfc0nE1ifiSBe98de4HSZemECU+yWIx7QVqa83apYULzQL0sDCzGBrgo4/MY0JCzNfHuon7nfCqLy42QeL++0++3V1CQkwrhEsvNQvR9+2Dmhp45RUz3XjwIAQFQXKyWYc0dOj5n9PlMuuxbDbzc7HZYPjw8z+uiIicTCFKfJbVarbf6dMHBgw4vr/hifffe+/xr196yWzN8uCDZqotKAi6d4dPPmnfOqOjzQLz3FwT8g4cMKEtKAhSUswVc4cPm6AzdKhZ6P3QQ+aKwBEjWnebazCsAAAgAElEQVSu0lL4z/80P4/f/Q5Gjmyf5yQiIlpYLj7OZjM9nWbMOHdzzx49zJTaD39orjibMcNMCVosbW8M2lJJSaabO0C/fmYErLbWBKv8fLDbYdo0c39hIWRnm5YON99samyJjz+GCy6At982a7BERKR9KURJlzFxohnl+de/YOBAWLECbr214+vw8zMh58TgZrMdn06cOtWEq5/9zKyp6tfP9K06myVLzIL64GDYtMmMdImISPtSiJIuxWKBK64wQWr7dnOVnzeKjDRXHe7cCVdfDf7+Z37skSNmCu/SS01X99ZOAYqISNtoTZR0WYMGebqCc0tPN1vJHOuH9fHHZloyLe34Y7p3h/feM2uqji2iFxGR9qcQJeIDLBZobDQ9r+rqzFTkli1mjdfs2XDJJZ6uUESk69F0noiP8Pc3myNHRJhF6vfcYzZpPl3XdhERaX8aiRLxIf37m8Xxt94K8fFmS5n2vrJQREROTyFKxMfExJitakRExLM0nSciIiLSBhqJEpEuKzc3lzVr1uB0Ohk+fDjjxo3zdEki4kM0EiUiXZLT6eTdd99l9uzZ3HXXXWzfvp2ioiJPlyUiPkQhSkS6pEOHDhETE0NMTAx+fn4MHjyYnJwcT5clIj5EIUpEuqTKykoiIiKav46IiKCystKDFYmIr+mQNVHZ2dkkJSW1+PG1tbWE+GjrZdXuGardM85We3l5eQdXc/4sJ/SLyMrKYsuWLRw5coSAgAD8/PyIioryYHXnz5dfa+D79YOegyc9++3fLam/pe9fHRKiHnrooVY9fuHChdxxxx3tVE37Uu2eodo9w5dr/+7IU2VlJeHh4c1fZ2ZmkpmZ2fy1Lz/XY3z9Ofh6/aDn4A3cWb+m80SkS0pKSqKkpISysjKamprYvn07GRkZni5LRHyIWhyISJdks9m4+uqrWbRoES6XiwsvvJCEhARPlyUiPsQ2fvz4BZ4u4nRas4bK26h2z1DtnuHLtcfGxnLRRRcxevRoevbsec7H+/JzPcbXn4Ov1w96Dt7AXfVbFixYoO1LRURERFpJa6JERERE2sCr1kT50hYMK1euZNeuXYSGhnLXXXcB5rLJZcuWUV5eTlRUFLNmzSI4ONjDlZ6qoqKCFStWUF1djcViYcSIEYwePdon6m9sbOSFF17A4XDgdDoZOHAgEyZMoKysjGXLllFXV0diYiLf+9738PPzqpd3M6fTybPPPkt4eDizZ8/2mdqffPJJAgMDsVgsWK1W7rjjDp94zbTWud6HmpqaWLFiBQUFBYSEhDBz5kyio6M9VO2pzlX/J598whdffIHVaiU0NJRp06Z5XeuGln4WfPPNN7z++uvcfvvtJCcnd3CVZ9eS57B9+3Y++ugjLBYL3bp1Y+bMmR6o9PTOVX95eTkrV66kvr4ep9PJxIkT6devn4eqPdXpPqNP5HK5WL16Nbm5ufj7+zN9+vQ2TfF5zZoop9PJ4sWLmTNnDuPGjWPNmjX07NmT0NBQT5d2WkFBQVx44YXs3LmTUaNGAbBu3Tri4+O5/vrrqaysJC8vj969e3u40lM1NjaSmprKFVdcwQUXXMBbb71Feno6n332mdfXb7VaGTJkCKNHj2bEiBF88MEHJCQksH79ei688EKmTp1KXl4e1dXVXvemesymTZtwOBw4HA6GDh3KqlWrfKL2Tz/9lNtuu40xY8Y0X/rvK6/5lmrJ+1BWVhZ2u525c+cSEBDAZ599xqBBgzxY9XEtqb+pqYkJEyYwevRoGhsb+eKLL7ymfmj5Z4Hdbmf16tWEhYXRt2/fkxqnelpLnkNJSQlr1qzhlltuYcyYMfTo0YOAgAAPVn1cS+p/77336NWrF9OmTaNnz54sX76ciy++2INVn+x0n9Enys3NZffu3dx+++0kJiayevVqRowY0erzeM10nq9twZCWlnbKb9w5OTkMGzYMgGHDhrFz505PlHZO4eHhzYk7MDCQ+Ph4qqqqfKJ+i8VCYGAgQHMQsVgs7N27l4EDBwLeWzuYUcDc3FyGDx8OmN+GfKX20/GF10xrtOR96MTnPHDgQPLy8nC5vGNpaUvqT09Pb/6wTklJ8bou7S39LPjwww8ZO3asV47atuQ5bNmyhZEjRzZ/joSFhXmi1NNqSf0WiwW73Q6YQHtijzVvcLrP6BPl5ORwwQUXYLFYSE1Npb6+nqqqqlafx2tefafbguHgwYMerKj1qqurm19I4eHh1NTUeLiicysrK+Pw4cMkJyf7TP1Op5OFCxdSWlrKqFGjiI6OJigoCJvNBnj39h1r1qxh0qRJzW8+tbW1PlO7xWJh0aJFzVPAmZmZPvOaaamWvA+d+BibzUZQUBC1tbVeMWre2vfRL774gj59+nREaS3Wkudw+PBhKisrycjI4JNPPunoEs+pJc+hpKQEgOeeew6n08n48ePp27dvh9Z5Ji2pf/z48SxatIjNmzfT2NjI3LlzO7rM83KmbZ9aGwa9JkSdzolbMIj72e12li5dypVXXklQUJCny2kxq9XKj370I+rq6njttdc4evToKY/xxtdOTk4OoaGhJCUlsXfv3jM+zhtrB7j11luJiIigurqaRYsWERcX5+mSOkRL/nt4638zOHNtX375JQUFBdxyyy0dXFHrnfgcnE4na9asYfr06R6sqPW++9/B6XRSWlrKvHnzqKys5Pnnn+fOO+/02jWF363/66+/ZtiwYYwZM4b8/HzeeOMN7rzzTqxWr5ngarW2/H/sNSHqXFsw+IKwsDCqqqoIDw+nqqrKK34zPROHw8HSpUsZMmRI81SSL9UPEBwcTFpaGgcPHqS+vh6Hw4HNZvPa105+fj45OTnk5ubS1NSE3W5nzZo1PlE70PxbW1hYGP379+fQoUM+95o5l5a8Dx17TGRkJA6Hg/r6eq/54Gvp++iePXvYsGED8+bN87rpsHM9h4aGBoqKinjxxRcBMwPwyiuvcOONN3rNWsKWvo5SUlKw2WxER0cTFxdHaWmpVzyHltS/detW5syZA0BqaipNTU3U1tZ61bTk2bgrc3hNZOwMWzBkZGSwbds2ALZt2+a19btcLt58803i4uIYM2ZM8+2+UH9NTQ11dXWAWSCfl5dHXFwc6enpZGdnA95b+8SJE/npT3/K/fffz8yZM0lPT+e6667zidobGhqapyAbGhrYs2cPCQkJPvGaaY2WvA+d+Jyzs7NJT0/3mpGoltR/+PBh3n77bW688Uav/MA713MICgrioYce4v777+f+++8nJSXFqwIUtOy/Q//+/dm3bx9g3tdKSkq85irPltQfGRlJXl4eAMXFxTQ1NfnUL1EZGRl8+eWXuFwu8vPzCQwMbFOI8qpmm7t27WLNmjXNWzBceumlni7pjJYtW8a+ffua10JMmDCB/v378/rrr1NRUUFkZCSzZs3yyp2u9+/fzwsvvEBCQkLzm/8VV1xBSkqK19d/5MgRVq5cidPpxOVyMWjQIMaPH09paelJbQJmzJjhdb9hn2jv3r188sknzJ492ydqLy0t5bXXXgPMNMSQIUO49NJLqa2t9frXTGud7n3oww8/JCkpif79+9PY2MiKFSs4fPgwwcHBzJw5k5iYGE+X3exc9f/f//0fRUVFzQEqMjKSm266ycNVn+xcz+FEL7zwApMnT/aqEAXnfg4ul4u1a9eye/durFYr48aNY8iQIZ4uu9m56i8qKmLVqlU0NDQAMGnSJK9aX3e6z2iHwwHAyJEjcblcvPvuu+zevRt/f3+mTZvWpteQV4UoEREREV/hNdN5IiIiIr5EIUpERESkDRSiRERERNpAIUpERESkDRSiRERERNpAIUpERESkDRSiRERERNpAIUpERESkDRSiRERERNpAIUpERESkDRSiRERERNpAIUpERESkDRSiRERERNpAIUpERESkDfw8XYCIiDf761//SlpamqfLEJEOlJ2dzUMPPXTOxylEiYicRVpaGllZWZ4uQ0Q6UFJSUosepxAlIp3KypUr2bVrF6Ghodx1112n3O9yuVi9ejW5ubn4+/szffr0Fr9hioicSGuiRKRTGTZsGHPmzDnj/bm5uZSWlnLPPfdw7bXX8s4773RgdSLSmShEiUinkpaWRnBw8Bnvz8nJ4YILLsBisZCamkp9fT1VVVUdWKGIdLiKg7D/E/O3G2k6T0S6lMrKSiIiIpq/joiIoLKykvDw8JMel5WVxZYtW6ioqCAzM5P58+czf/78ji5XRM7X0V2w4QmwWMDqB5f9HCJT3HJohSgR6fIsFsspt2VmZpKZmcmqVau0sFzEVzkaYdPfzQhU2iVQvh/KD7gtRGk6T0S6lGMjT8ecbhRKRHxYbamZunO5wOYPA6dCRJIJUFY/iOrhtlNpJEpEupSMjAw+++wzBg8ezMGDBwkMDFSIEukMqgtNeCrOAasNEgZAcDT0vhzi+pkRqKgebhuFAoUoEelkli1bxr59+6itreWJJ55gwoQJOBwOAEaOHEnfvn3Jzc3lL3/5C/7+/kybNs3DFUunUXGwXT6o5RzslbBrLZTsBlsA9LgIUkZBQOjxx0SmtMt/E4UoEelUZs6cedb7LRYLU6ZM6aBqpMuoOAjrfw/OJrcvXpbTcLmgsQYCwsAvGOrKIf1SSB4BfkEdVoZClIiIyPlwuWDvx+BoguieULrXrYuX5QQuF5Tkmmm7pnoYNd+sexr5n+bquw6mECUiItJWjkbYtRqOfG2mlQ5mQX0FhHf3dGWdi8sJRdlwYBPUHIWgKOgx2oQqCx4JUKAQJSIip1FSUkJxcTHx8fHExsZ6uhzvVF8B25dDdREMuAYie8ChLMj/3ISq6HSPfbh3OiW7YccqCI2DAVPNonGL5xsMKESJiMhJSkpKWLx4MbW1tcTExHDdddcpSH1XRT5sf8OsgRoyE2L7mNujUiE0HvZtgJh06D7Us3X6KkcDFGwzV9kljzA/3yHXQ0wvrwqmClEiInKSoqIiCgoKCAkJwW63U1xcrBD1XVZ/CAyHgdMg5Ds/m55jTE+iXWshIvnU++XMGuvg0BY4+LlZ8xSfYUKUxQqxvT1d3SkUokRE5CROpxOLxUJAQACBgYHEx8d7uiTv4GwyPYi6DTJrnkbccvpREYvVTDl98X9mqk8hqmWOfA2575lRqNg+0ONir1+crxAlIiLNXC4XR44cITMzkwEDBtCtW7eT9hrssuyVZvqu6rAJReHdzz6tFBgOF/3QtDuQMyveadpDxPc3jTFje5vwFNbN05W1iP7riohIs6NHj1JWVkZmZiZpaWl8/vnn1NTUcNlll512j8EuoSIfvllhRkgGzWj5lXfHAlThN6YJZFzf9qvRF1UchFX3mJ9NdJrprTVwuqerahXPL20XERGvERcXxyWXXIIlPJYNu4to9Avm6NGjFBUVebo0zzj8JWx7xXzQD7/ZrNFpDZcTDn4GO1dBfXn71OirindAQw1Eppqp0vIDnq6o1RSiREQEMFN5FouFxsBwfrPmG57btJvntldix0Z2djYul8vTJXY8m78ZJRl+s7nqrrUsVrP43OWC7LdMqJJvWczPp7HW7RsDdxRN54mICACbNm0iLi6OvY4w6hubsFgsVDc4COqRSknxPoqKiujWzTfWqpyXhmqoPGym3xIGQvyA87usPjgG+l0JO94yrQ/SL3Nfrb6sqR56XwF9JplO716+iPx0NBIlIiIcPXqUgoICmhwuPsk7SlG1nW8OV1BR38CIQf0ICQkhNzfX02W2v8oCyHoBdr5tPuTBPX2Jug0yPaP2f9tx28vll9WwYXcR+WU17XMClwvK9kPiUEgb65MBCjQSJSIiwM6dOwkMDGRHXSBWi53fXDOUD3YWUlHfQHJ0GJEXX0xYWJiny2xfh780vZ0Cw2HoDe7fyLbvJDO6FRrn3uO6WX5ZDQ+s+IKC8jr6JYSzYMpQUqND3X+i/lPMdKkP00iUiEgXV1paypEjR/CPSeTrw5Vc2ieBi9Li+X5mT4L9/dhVVEl0dDT+/v44nc7OtzbK5YLc9yHnXdNxfMTNEJbg/vPYAiCun/l3bak5rxfaV1JD3tFqbFYLZbUN7Ctph9Eoi8WsNYtIdv+xO5BClIhIF7dz505cFj+2VvmTEh3CpX1MgEiLCSM8yJ+vD5mryqqrq3n//fc5cuSIJ8t1P4sF/AIgdZQZgfIPad/z1RRD1nPmqj0vlBYbSrirnobig9RWlZMW2w6jUIXfmBYHPk7TeSIiXdzAgQPxj4ynoMjBjAtSsVnN79dWq4XhqdFU1jficrkICQnB6XSyY8cOunfv7vt9o6qOmEvrI1Mg7dKO25MtJM7sAZf3kbm8PyKpY87bQiHOeq4MOMCuukJsZREEO8YCbgxSLhfsfh9ievvsWqhjNBIlItLFRUVFMXJQX+6+LIOY0MCT7pvQrzvThqZisViwWq3079+/efrPpxVuh62LYPe/zId6RwZCiwUyroaAMMh+8/gCdi9RXFxMVelREsMD6BXlz9Gjbl4IX1Ns9siL7une43qAQpSISBdVWVnJe+v/zb+278fpdGG1njlIlNXaAejZsyehoaG+2zfK5TTBaccqMwI0ZFbHBqhj/INN/yh7JeSs9qr1UV8etXO0qo5Gez0BNqv7904s32/+jvL9EKXpPBHpdHJzc1mzZg1Op5Phw4czbty4k+4vLy9n5cqV1NfX43Q6mThxIv369fNQtZ7zzY4drPtiB1H9QxnTL5mQgNN/JGTtL+Ht7Ye4b0J/okICGDBgAFlZWRQWFtK9ewu3QPEGTXb45g0o2wfJmdD7crDaPFdPZAqkX/rtSJQL8I7p0T2F5cSmpDFl7AiC45P5oqiBSe7cQ7lsv+mdFRTpxoN6hkKUiHQqTqeTd999lx/84AdERETwj3/8g4yMDBISjl9t9fHHHzNo0CBGjhxJUVERixcv7nIhqrq6mo+37qApLI7rMnufMUAB9I43rQ22Hy7nkt4J9OjRAz8/v5N+pl6t4qDZUiQy1bQtyLgaEi9w+2lqa2upqakhNDSUkJAWLk7vcbHb6zgf5bUNVDfCgH79GD16NJ/uPcrG3YcY0SPmlKneNnG5oLrQbDTcCShEiUincujQIWJiYoiJiQFg8ODB5OTknPSBb7FYsNvN9JTdbic8PNwjtXrSR59t42B5HZMmj6NP/Nmff3RIID1iQvnqUDlje8VjtVpJSfGRBcEVB2Hdb8HpNFfgXfqQaWPgRi6Xi4qKCrKzs3E6nQQGBjJo0KCWBykwmxzv/8RscOzB3kn7S2sICI9m8ri+uFwuinduofZIPTsLkxnTyw3TehYLjP6R2cy5E1CIEpFOpbKykoiIiOavIyIiOHjw5Eupx48fz6JFi9i8eTONjY3MnTu3o8v0qKrqatZv/YaEpBSuuiC9Rd8zJCmKd7YforCqnu4RwQDs27eP/Px8s2Gxt16pV37AdCEHCE80YaWNIcrlctHQ0EBAQAAWi4Xy8nJKSkqw2+3U1NRQU1ODv78/AQEB1NTUUFVVRVVVFQEBAQQEBDTfFxYWdurPy9EIpXmw5wOzRYyH7Mw/QqDVSUJ4EBaLhbioCALyjrDzSIV7QhSY/fLc3cjUQxSiRKTT++4H1tdff82wYcMYM2YM+fn5vPHGG9x5551YrcevtcnKymLLli1UVFSQmZnJ/PnzmT9/fkeX3i78/fz4j4uG0adPH/xtLbu+aFBiJKuzC/j6UHlziLJYLBQWFlJQUEByspc2TYxIBnuVWcht82/VJrcNDQ1UVlZit9upr6+noaEBp9NJ7969CQoKwmq14u/vT1hYGDExMTidZnNhq9VKaGgodrudwMBAGhoaqKmpwel04ufnR0ZGBgAFBQU0NDR8G67C8Y/LJDB/K8FRPSFhQLv8OM6l+uBuIhoasViGAZCSkkL017vIO3SE2oa0s077tkjOagiJgdSL3FCt5ylEiUinEhERQWVlZfPXlZWVp0zXbd26lTlz5gCQmppKU1MTtbW1J21rkpmZSWZmJqtWrSIrK6tjiu8A1fZGwoKCmHhJ6z7EQgL8mDMyjeTI41NUqamp7Ny5k+zsbJKSkrxzNMrZCKmjIXm4WX90Ql8il8tFU1MT9fX12O325j8JCQmEhYXR0NBAYWEh/v7+BAYGEhoaSmBgIH5+5qMzIiLilFHPE9dEhYSEEB0d3Xwuh8NBU1NT8+P9/PyaA1Z5eTmQSLCtiF67VkN4dw6V1uJ0OptHsI7V0dTU1Pq1Vy1QXV1NpMXO2BGDm29LTEwkISKEmrpyymobzi9EOZtMa4mkC5tvatM6Mi+iECUinUpSUhIlJSWUlZURHh7O9u3bue666056TGRkJHl5eVx44YUUFxfT1NREaGg7dGX2MqU1dv73zQ1cOrAHE4e1fiF9r7iTw6jVamXAgAF89tln3jsaVbid2pAkKuPGYmvwx1pSQlBQUPNI0Z49e5of6ufnR1BQUHMYDAkJoX///thsLbuC71hwOh2LxYKfn19zAANOWqfndDppbGzEmRwH3yyBgi8gqD8NDQ1UV1c3j3IFBgZSXl6Oy+XCYrEwcOBAt4WPXXl7cbpcpKYen+709/end49kYsvLSYoMPr8TVBw0QSo6DTABavv27bhcLmw2m1ufS0dRiBKRTsVms3H11VezaNEiXC4XF154IQkJCXz44YckJSXRv39/Jk+ezKpVq/j0008BmD59uneOoriR0+ni9aw9VBfk4dcjBGjb1YhZ+0uoa3Qw7tutYVJSUtixY4d3jkY5GqgtzOPLxnSqt38DQHx8PImJic2jSomJiQQGBp40wnTMidO77c1qtRIYGAiB3WH4zRAcQ/K3P8tjo1gNDQ2UlZVRVlZGcHAwdXV11NTUuCV4uFwuPtySTUmdg1nfOV6/fv2or6/H4XBitVrP2k/srMr3m/VQkSak1dTUUF9fj7+/P1ar1W3PpSMpRIlIp9OvX79TWhZcfvnlzf9OSEjgtttu6+iyPGrDniL27N5N37hQhg8dfO5vOIOD5bXsOFLBxelx+NmsWK1Whg0bhtVq9a4ABWALoLLXFGpy9hEcHIzVaqV79+7NI0AWi6X5Kk6vEvJtU6b6CrBXYYlMOWkUa9++fZSXlxMVFeW2EdSqqioKS8roPWDIKf8d4+PjKa6q54l1O5k+NJWMbhFnOMo5lO2H8O7gZ1olhIaGYrFYqK6udutz6UjqWC4i0skdLKtlXU4BUY1lXNi/90nreFprSFIU9iYnu4qqmm/r1q2b+7tau4l/WBwWmwkgAQEBREREeF/YO5Odb8P25dBQ3XzTsSnGqKgoevTo4baRG5d/EEG9hjO4X6/T3h9kaaKm6BA7jpS38QQuCI6G+P7NNzmdTtLT04mKiiItLc3nRqFAIUpEpNOrqG8goKqIXjHmA/h8pMeGERbox1eHyk66vampia1bt57STsJj7FWQ/SaxwTB69Gh69+7te2tu+k42/ZR2rDppW5iYmBgiIiJobGx026n2ldRgCwyiT7eo095fWlKCf1k+3+w9hNPZhi1qLBYYcG3zVXkul4sjR47Q2NhIaGgoDofjfMr3GIUoEZFOblBiFLMye9Onl/mt/3xYrRaGJEWRW1xFXUPTCbdbKS4ubm446WmOI99QcigPl8tMG8XHx/tWgAIIjYe+k8w2NfmfNt9ssVgIDw+nqqrKLT/r4uJiPt64kQCa6BZ++v5NiYmJxIcHU15cyMHy2tafpKn+pCB47ErIyMhIQkJCqKmpaWv5HqUQJSLSSeUUVrLtYCkul4u+ffuQmZnpluMOTY6mZ0woNd8JUQMHDqSyspJDhw655Tzno3B/DoWWBOw231tnc5LuF5ieUXs/hsrjP9eIiAicTqdbwse+ffuIsdqZOiztjFOdAQEB9E9PpbGyhB1HKlp/km2LYcdbzV9WVVU1h8Hw8HBsNptXhO/W0sJyEZFOqKq+kTe/yic8wEaUo4oeqaluu9osMTKYuRedunYmOTmZyMhIsrOzSU5O7tCr205UXXSAsqp64tIGEhTk452xLRbTwTwgDELimm8ODQ2le/fu5/38mpqaKCgooF96TwYnn32RfXrPHvTK3UdyUCvDTmMtVBdB/PEGopWVlQQHB+Pv709sbCyxse7c4bjjaCRKRKSTcblcrPwqn0aHi5HRDrI+/5yysrJzf2MrVdsbT5rSs1gsDBgwgKqqKo+tjXI6nRzO206gzUl8b/dvMuwRfkHQZ6K5qs3ZBN/2iIqNjcXf//z22Tty5AilVbU0hkSfc61TYmIiPWLDCaGV+96V7Td/R/cETHBraGg4pQmuy9WGtVYephAlItLJfLa/hD3F1Uzsl0Bh/j7i4+Pd/pt+tb2RP324ky35pSfdnpycTEZGRnOn7o5WWFhIo8uPpKQUrEFtvwrRKzXUwJYXYdca2P8JzrJ8ysvLqa+vb/MhDxw4QHGdk48P1XOuixYDAgK45ppr8Ivqxt6j1Wd/8InK94MtwOxdCM1b35z4GikuLiY3N9fngpSm80REOpGq+kbe33mYfgnhxLmqyK+rc9taqBOFBfqTHBnMV4fKGdsrvnktjcViYciQIW4/X0tFRkbi33c4IXFx536wr/EPMRsVf/R7iOkFAWEc7jGbqO49SUxMbNMhY2JiaAyvoXfMaTZFPg0/Pz/W7tgHuLg9rm/LTlK23+xZaDk+bvPdqV5/f38aGxux2+0+NQWrkSgRkU4kPMifWRf25JrBSezatYvY2NiTthdxp6HJ0RRV1VNYdepISEVFBVu3bu2wxcLHRjBCXLXExXphA013sFggPgNcTrBYsLoaCXPVUFVV1eYRnKSevWkM70ZabNi5H4z5OVsO72TXjmyq6lvQYsHlgvRxkDwCgMbGRvLy8qitPfkKv2ONNqurWzHC1QolJSXs3LmTkpIStx5XIUpEpJOotpsPtYxuEVgdjdhsNvr3799uzSUHJUZitVr4+tCpDeDlb6gAACAASURBVBirq6vZs2cP+fn57XLu7yoqKuJwwUFcX7wEue93yDk9Iq4fhMTA0Vyw2AhPSKGxsbFNU3rFxcXkFZsr7dJiW3YVo8ViITk6jIbyYna25Co9iwUSBpqRM8xVeXV1dafsR+jv709QUFC7tDrIz89nyZIlrFu3juXLl7s1SClEiYh0AnuPVvO/63ayp9h0Eg8LC2PSpEl079693c4ZEuBHn7gwth8uP2UkJCkpiaioKHbs2NHuo1F1dXWUlJTgqjiMxWGHlk4z+aLIFBj/c+g+BIZcT3hiHywWC5WVla06TG1tLevXr+fL7J0EB9jO2B/qdAb0TiOAJr7Ma8HFA6V7oOZo85dVVVUEBgYSEBBwykNDQ0Opqalx++tl69atFBQUEBMTg8PhoLi42G3H1pooERE3yS+rYV9JDWmxoaRGd1x/orqGJlZ8mU9UcAA9YkJPuny8vU3qn0iA36n75h27Um/Tpk0cOHCAtLS0djm/y+WioKAAPz8/utXvh4BQiG6fc3mN1ItMI87odGxWG6Ghodjt9lYd4sCBAwDMGHsB+Ae1arQyKSmJ+PBg9ufn43AOxXamVhYuF+SshohkGPQ9mpqaqKmpITY29rTni4yMxM/Pz62Ly+vr66mqqiI8PJzS0lJsNptbtyhSiBIRcYP8shp+9e5X7CutITzQn19PGcrgpKh236fN5XLxzjcFVDc08Z8jeuNntbB582b8/f0ZP358u54bIP4sIxhJSUlER0ezc+dOevTo0S59o4qLi6mvr6dHYgK2r/MgefhJC5g7JYsVYvs0f5nahh5g+fn5xMbGntJmoCUCAgIYNaAXNdVVWM/2+q4rNdvvfNva4NjarTPt3RgcHExwcHCr6zmbffv2ERgYyE033UR9fb3br1RViBIRcYN9JTXUNpr9v/YereZPH+5kcFIUPaJDGJUWS8+Yli3cba2vC8rZXlDOFRndSYoK4dChQ1RUVDBq1Kh2Od/p5JfVsGnvUWZckIqf7fiHucViYeDAgZSUlOB0Ot0eohwOB6WlpURGRhJuLwCXA7oNdus5vJbTAfv/DaFxWBMGAt8u+m5BaK+oqKCiooKYHn1Z+WU+Vw5MIsjfds7vO1H/fn2bQ9EZz1m2z/z97cigv78/UVFRZ736zuFwUFtb26Zwdzp9+/YlKiqq3aa1O3lcFxHpGGmxoYT4+5Ec+f/bu/PgOM/7wPPft++70d1AA924DwIkCB4gQYmmRFnWYUucbKRdyfHYshJ7M5FT8cwmrtSWk/1jk6mtqdk/piqV3XhTsuIkHiVxLI9HjmRxqEimblIHb4IHiPs+Gt0N9H2+7/7RAkgQd6MbAOHnU8USAbx4+wEJ8f3heX6HiTavnWcOVlPnNDMyEyOWygVX47Nx/vGzfj7omWIoECWTzS/3IxaL4fP5iMVihBJp6lxmHmgoQ1EUbt68icVioaqqqpBf3opSGZnr47Pcmgov+pjH46GtrQ2NpvA/s6vVahoaGnIPyIp9cPA5sJQX/HW2JUkF010wcg7I7cgNDg6u6VPHx8dRqVTMqKzcnAyhU68/FKioqCBlKeNvP+4ju1wO08wg6G1gyPWDslgsVFZWrhjozczMMDQ0VLDhymq1uqh5gWInShAEoQCqHWb+7MS+JXOi5nI8YqkMM7EU3Z8HGxq1RKXdxNMHqnCY9GvaSQiFQpw/fx6j0YhGo+FQayvH6stQqSTGx8cJBoN0dHRs6siVepcFi17D1bEgrR77oo8risLExASKouD1egvymslkEp1OtzBBuaSmIPe+J0hSbtet712IB5AkiWg0SiqVWjJp+04tLS14vV5+fGGcWqcZlSq/I2c5k6G3f5DBXeU0lN21c6QoMDMMrkaQJFKpFCqVatVg+s5WBxtp2CrLMh9++CENDQ1F/YFC7EQJgiAUSLXDzPEm96Kk8rnAqLHMyne/2ML//lgr//ZwLUdqXGQVBbMu92B5t3uSFz/s5n9cG+X6+Ox8y4I7TUxMkMlkCESThOIpotHo/ENwamoKs9lMTc3mBhMqlUSbt4RbU+EFY2DudP36dS5fvlyQyqtkMklfX9/tUvWxC9B7Ovfg/nXibgUkmLw+f/wVDi/eDbybJEmgNeKPJqlz5l8AYUiFiA53cbFniTYWkgT3fwfqHwJy35t9fX2rJo3r9Xq0Wu2GWx2MjY0xNTW1oXushQiiBEEQNplZr2F3hZ2vtHr5d8ea0Gly+SgOkw6DRs2F4SCvXBjkv7x9gx9+eHsURjyVwev1EktnGZueYXQ2Pv+TO8CBAwd45JFHtmTw735vCVlZ4foSvYPmcqOi0eiaj5yWM1eNJ0kSdrs9FziNnIfQKKvOLdlpDPbc7ttkJ3qdDoPBsGoQdfXqVa5cucJgINfUcq1NNpdSW12F06yns6d/6eBIYwC9DVmWCYfDWCyrd0WXJAmz2UwkEsm7Sk9RFLq7uzGbzQXb+VyOOM4TBGHH6e7u5tSpU8iyzKFDhzh+/Piiazo7O3n33XeRJIny8nKeffbZLVjpQgernByscpKVZcZnEwwGIqSzt4/4/u5sL+OhBL2DaZpMMsG0hoeTCiZTrpTbYDCg1+u3ZO0eu5GWctuy+TUVFRU4nU5u3rxJbW1t3oFeMBgkFotRWVmZa+EQnoDYNDR/ZSPLv3d59sP0LcjmBvpOT0+TyWSWPDbLZrP09/dTUVGBAfDajevqD3U3nU5HU20Vn3WPMBGK47Gbbn+w791ckOdtn+/9tFxV3t0sFsv8TMB8qvX8fj9+v5+DBw8W/QcKEUQJgrCjyLLMyZMnef7557HZbLz00ku0tLQsGH3i9/v58MMP+d3f/V2MRmPRRk3kS61SUeUwUeW4/VBKpVK0GOKEIllGkxraK0xMJyQG/FGM2Tjvv/8+DzzwAOXlW5NYLUkSX++oW/Hje/bs4aOPPmJgYICGhoZ1v0YqlWJychKLxZLbhQKY7ARJDWV78lz5Pa68bb4i0Wazrbh7MzExQSqVorq6Go/Hwf7KjQ+JPrS7kb7BEUIzM7eDKDkLo+egPDdDMRQKoVarF+yarsRisdDU1LRqbtdybt26hU6nK1pvsjuJIEoQhB1ldHQUp9OJ05mbn9bW1kZXV9eCIOr8+fMcOXJk/qdci6U47QcKye/347Hq+FqDl56ZNNfiWTQqNXUuMzevXUSr1Ra0/02+0lmZ2XiKUsviHY6KigqqqqryrtRLp9NoNBq8Xm9ud06RYeo6uBpAW9j+QveceACD3o5hhSB6eHgYvV5PaVnZmtshrKaproa2Sgck7jhGDI/nBiU7alEUZb7Z5VpfT61WLxoLsx719fV4vd6iVITeTQRRgiDsKKFQaMGxgc1mY2Rk4XiKuYTkH/3oR8iyzMMPP8yuXdt3VEg6nSYYDFJSUoK3rGS+CrDWacIkJ5iYmChaG4H1+sm5ASLJDH/wUPOij0mSxNGjR/O+t9lspqmp6fbDOJPM9SD6dd2FmjMzCJf+CfZ9FcXZSDQaxWg0LghEUqkU4+Pj1NfXc3MyzMnrY7zwQBMO08aOf3U6HU8++SRxWUU8lcGo09zuD1VSgyRJNDY2rju/KR6PEwgEqKioWHdA5fF41nX9RojEckEQdry7fwKWZZlAIMC3vvUtnn32WV577TXi8fiCa86dO8eLL77IjRs36Ojo4Ic//OFmLnmBuVlfc+Mqqh1m7q8pIekf59KlS+h0OhobG7dsfXfaXW5jKpxgMhRf9pq53JxsNrume6bTaaanpxfvnmiNsOc3d/asvLWwVeX+LCY7icfjDA4OLjqizmaz1NbWUltby0AgiqKA3ZDfcdndEoqK/+fdLq6OfT6IemYQLG7Q5o73tFrtuo/mZFlmZmaGWCy29nUkEnR2duY1jDlfIogSBGFHsdlsC4axhkKhRd2PbTYbLS0tqNVqHA4HpaWlBAKBBdd0dHTwne98hz179nDu3DleeOGFTVn/3dLpNDMzMzgcjgWz8LRaLbFYjOHhYZqamjZlTt5a7PWUoFJJXBmdWfaa6elpzp8/z8DAwKr3UxSF8fFxfD7fwgaM2TREil/Cfk9QqXPtDqa7MWpzvZjurtIzGo0cOnQIh8PBgD9KjcOUd3+ou5UYdUhTfZw5fzlXLanWgasJRVEYHR3Nq12B0WhEpVKtK1+xt7eXrq4uUqnUul8vXyKIEgRhR/F6vfj9foLBIJlMhs7OTlpaWhZcs3v37vkHeDQaxe/3b6ixXzFpNBpqa2spLS1d8H5JknC5XBw8eHDb7EJBrn1DU6mFq2Mzyx7huN1uXC4XN2/eXHU3KhQKEQ6HcbvdC3cz/N1w7ke51gYClO8FOYPkv4XVaiUcDs/35EokEvj9/lx+UiKd6w+1gdYGd5MkiXKzhoHBQeLpLOz7KtR/kXg8zszMDJnM0r3DVqJSqTCbzWsOwDKZDH19fXg8njVXARaCCKIEQdhR1Go1J06c4OWXX+YHP/gBe/fuxe12c/r0aW7evAlAU1MTRqORv/qrv+LHP/4xjz/+OCaTaZU7b425vjl37zTJsozFYkGr1RakgWUh7a90EEqkGQ4ufRQz1zcqHo/T39+/7H0ymQwTExOYTKb5QoF5k9dAbwVrcfsA3TOsXjA6YPI6NluuN9NcADI0NMQ777yT69MVyL1vI002l3JwdyOZZJyL3UPz7wuFQkiSlHfhhtlsJplMrmlnaWBggGQySXPz4ly8Ytr6LERBEIQCa25uXvSP6SOPPDL/e0mSeOKJJzZ7Wes2N+NsqbYF58+fJ51OY7fbCYfDefXTKZaWchsvPNCEx778mtxuN6WlpVy4cIFUKkV5efmi6sKJiQmy2Swej2dhLlQqCoE+qL7v16/B5nIkCVqfAkMJZo0BtVpNJBLBarUyPDyMw+HAYrFQJsd5sLGMClthv1/aWxr42ZvvMfbBfwXN/SjNT8w32My30s5isaDX68lkMivmVMmyTHd3Ny6Xa9MrVEUQJQiCsA2lUimCweCSx4yRSITh4WEaGxtxu93bKoAC0KpVeEtW3tmTJAmPx8O7777L5OQker2eZ555ZsFD0Ol0YjabMRjuapfgu5lrb/B5fyThc9ZcVZpErsxfp9MRCoUIBoMcOHAAgHKbkfICB1CQG9fyxb11qLtvoWhNJBIJUqnUomPo9d6zqalp1esymQwOh4Pq6uqCtG1YD3GcJwiCsA1NT08jSdKSD6HPPvuM6elpSktLKSsr25Z9ruKpDK9dGaF7KrTsNbIs43a78Xg8ZLPZ+SrEuVwqk8m0dK6a72au+stcVpS139Omb8GN19Hr9UiSxPDwMJIkUV1dTTyVYSgQJVuk49/DNTZqXEZkezWyLGM0GhcVdeRDUZQVWyTodDqOHj1KZWXlhl9rvUQQJQiCsM2kUilmZmYoKSlZlAs1NDTEW2+9hc/n44033mB6eppEIrGpZd1rodeouTUV4sJwYNlr3G43Go1m/thuroXD+Pg44+Pjyz84256FPU8VY9n3vmQ418U9MsXU1BTd3d243W4MBgO90xH+9mwvk6HifK9UGmI47Xo+mzFhNptpaGjYcO+yWCxGV1fXohYkc0Kh0IJq3M0mgihBEIRtZqVdqPPnz6MoCg0NDfO7N0NDQ/O7ONuFSiXR5i3h1lSYRHrpCjyXy8UzzzyD0+mksrISp9NJJBIhGAyiUqmWP5rR6MGc/zHRjubeA5IKJjtJJpO0tLRw6NAhAAb8EfQaVcHzoeYFBxjKODh9pTeviryl6PV6ZFlettVBZ2cn77333pYVV4ggShAEYZtxuVx4PJ4lez8dPXqUiooKZmZmUKvVuN1uLBYL0Wg076n3xbLfW0JWVrg+MbvsNS6XiwcffJBsNsvIyAhjY2Po9fr5XakFFAU6/xv4uoq46nuc1gTORpi6jtViRpbl+SG8A4EotU5zwfpDLaAo4G0nYW3Ad/0z3jv7yZqbqa5ErVZjMBiWbHUQCoUYHx+noaGh6IOGlyOCKEEQhG1Gr9dTUlKy4H3ZbBZZlvF4PHzzm9/kS1/60nwitsViIZvNrqu782bw2I24zHqujAZXvK6qqgqz2cy5c+dIp9N4vd6lH4rhMZjuhmyySCveIcr3IsdDnP/wbfx+f67XViLNdKSw/aEWkCSoPEz7/Q+jlRQGxnwbmn93J4vFQjweXxSUdXd3o1KptrRPmgiiBEEQtolUKsXw8PCSfXGuX7/O6dOnyWQyuFwudu/ePV/JZrFYkCRpUZfqrSZJEvfVuSi3GlbcJZt7EE5OTiJJ0vI9uyavgUoDpS1Lf1zIcTUxKbkJzoYwmUyEw2EG/LnjsEL3h5oXGoVkGJNeS2mJjcGJ6YLtjJrNZhRFWbAblUgkGBoaora2dnH15iYSQZQgCMI24fP5iEQii3ZhQqEQ3d3d2O32JRN119vdeTPdX1fKk3srVy09b2howOPxLP/glbMwdT03J0+zsaG5O55ay7B5Pzqbm4aGBoxGIy1uK9/+QkPx8qGuvwa33iQcDlPtLUeWs/j8yxcVrIfJZKK0tHRBr6hgMIgkSVs+OFz0iRIEQdgGkskks7OzOJ3OBYGSoihcvHgRjUbDvn37lv18j8dTsOOTQlMUhbHZOJXL9I5KJBLo9XqefPLJ5ZsqBvshHRe9odYgk8kwNjZGTWUFTiPgzJX+1zqLdJSXmMn9quogHA5zsKURvZxiemoSd+nGm19KkrSo4azH4+E3fuM3Nlz9t1FiJ0oQBGEbWK4ib3h4GJ/PR1tb24rHFjqdbtsGURdHgrz0UQ+TocVl6n6/n6tXrzI+Pj4fQC3ZrkGtze1COeqLvdx73tjYGJlMhpqZj6HnV0QSad68OkQgWqRcsuBg7r+OOsrLy6murubRRx+loalwu0Rzx3mZTIZEIoGiKFseQIEIogRBELbc3C6Uw+FY9GAYGBjA6XRSX7968BAMBpmYmCjWMvPW4rahUklcHZtZ8P5oNMrly5eZnZ1ldHSUWCzG0NAQJ0+eXFzSXlKb6w+l2p6B4nZiNBqpq6vDVb8PAn109fXT29tDOFakXmIzg7mqQFMpRqMRi8VCfzjLfzl9k2iyMK0O0uk0AwMDzMzM8M4773Dx4sWC3HejRBAlCIKwxdRqNS6Xa8m+UA8++CBf+MIX1jTOIplMEggEtt1AYrNeQ2OphatjMwtyngKBANlsdr4reTQanW9tcOvWrds3iE5Dauk+QcJiZWVldHR0IFXsA0UmOtWDWiVhltKFfzFFye1EOWrxBwLzeXlui4HZkT7e+exSQV5Gp9Oh0+no7+8nGo0uOU9yK4ggShAEYYtpNBrKy8sX7EJFIhFSqRQqlWrNs/GsViuKoizbmHAr7feWMBtPMxS8nfyeTqeRJIlsNoskSZjNZoxGI7W1tQwODt4+1ut5Cy7+Y+6BLawoGAzeLjCwuMHiJjN+FZvFXJzvC0mCQ8+TrT7G5OTk/Gt47EZ02QRXrt8qWJWeyWTi1q1bmM1mPB5PQe65USKIEgRB2EJzFXl3kmWZTz/9lPfff39dDyCTyYRard6WQVRLuQ2dRsW18VzjTUVRyGazNDU1UV9fT2tr63xrg5aWFmRZpru7OzfGJDj4eSfuzR0uey+6ePEiZ8+enX875tiNKjyOx6YjFosVrJP4AoYSwlkdiqJgs9mAXDL47sY6JgMzTAcKU6WXSqUIhUJUV1dvWXPNu22PVQiCIPwaSiaT+Hy+Ra0JBgYGCAQCNDc3r2sqvSRJWCwWIpHItutertOo+fbRRr6yJ7eDIEkSTU1NVFdXU1ZWtqA3lMViobKykoGBAeSJTkCB8r1btPJ7RzgcJhAIUF1dPf8+v6WZy1W/RW11LYqiFL6X2PCnMHWdcDiMVqtdUPzQsacJGfjsWk9BXmpychKtVjvfH2072PrUdkEQhF9TPp8PSZIWPBQSiQSdnZ2UlZUteBiuldVqJZvNks1mt0X10p089tyx5FyAJ0nSshWF+/btQ6VSobr2T2D1gGn7PDi3q+HhYSRJWvB9U11exv/2ZGnumNeoxmwuYLNNRYbBj5Cdu4hIjZSUlCwI+ps9Dtp31ZGczTXeXM8PBEvp6OigtraWioqKja68YLbX/2GCIAi/JpLJJKFQCJfLtSDYuXr1KplMhvb29rweOna7HbvdXsilFtTZPh/BcIQmY5rq6upl873MZjPEgxDxoTQ+yr1wkDccjDLgj1LnMlPtKFJn8GUoisLw8PCiXT0AKRlC6v0Vtur7QW0r3ItGpiCTIGmtQhVTzR/lzVGrVDx2pI3JyckNB/VzLQ22Sy7UHHGcJwiCsAWW2oWSZZlkMklzc/OiB9J6FWL4azHMxFN0DU2QTGWWb6z5uZTazHuZg/TFi9QksoDODfp57u8/4v947RL/8eQVhoOb2z0+EokQjUYX7EJFkmn+6r0uembS4O9FHr/K9PR04XLmggMAGN2NNDc3Lzmux1tZhbFyF8F4/rlYiUSCt99+G5/PRzabZWJiYtvk/YkgShAEYQuYTCbKysoW/HSuUql44IEHaG1t3dC9g8EgXV1dxUki3qA2jx1dNsFMVr1qc1CtVous1nOrd2DbtW2YE09leKNzlP/vg1tEkhmsBg2xdJYB/+YGUVarlRMnTiwIogYDUaYjSYwGE5TuQpq6gX96mpmZmRXutA4zgyjGUhRdbnbjUjunGVnhv18a4kzXUN55er29vYRCIQwGAyqVipmZGUKh0LruMRyM8kHPVMGDW3GcJwjCjtPd3c2pU6eQZZlDhw5x/PjxJa+7du0aP/vZz/i93/s9KisrN3WNTqdzwdujo6OUlJRgNps3nDtiMBjmWx2UlJRs6F6FZiaFSaumLyyz9N/K52aHkQbP0ly3n7PnOxkZGaGmpmazlrkmiqLwo7O9+KNJHt7lRlYUZEVBp1ZT59rc4zxgUUf7AX8UnUaFx2aEbBvS1A2sRJkNK8iyvLEKN0WBTJKIuYrx7m5qa2vR6xfPNDRo1biUCB+9e5aHGsvme4KtVSaTobe3F4/Hg9VqBXJHvXPFE2v5f+XWZIj/9GYnkgQalYo/O7GvYMetYidKEIQdRZZlTp48yXPPPcd3v/tdOjs7mZqaWnRdMpnkk08+2fTgKZlMEgwGF/xUHovF+Oyzz7h69WpBXsNgMKDVarfNkcedZmdnqXBYGZhNEYqv0PxxohNmh/BW12Oz2ejq6to2FYdT4QSynHuAP77bw3ce3MVv39/I//UbB/i9Y7v40y+3ksxs3s7Z5OQk77333uIqT3+EGocZlUrKjcvRmrDFB5FleePDqiUJDv02Ycd+ZFlGq9Uue2l7cx3xTJZr3f3rfpmBgQFSqRQtLS3z77NYLKTTaVKp1KqfPx1J8IP3u+jxhal1msnIckF3CUUQJQjCjjI6OorT6Zwf5NvW1kZXV9ei606fPs0DDzyw6RVsPp9vPtF2zpUrV1AUhba2wgzX3a6tDhRFoaysjH1NNeyusJNaLm9LzoDvBpS2IGn0NDc3Mzs7u+UjbeKpDCevjfLXH9zi4kiu91FLuY0KWy45vtph5niTm25fhJ+cG8AfKdKsursMDQ0xOzu7YCcqmszgiyRv74ip1FB1BLOjHLVave7jsKUoikI4EsFisay4q9VWVYbWbOdKd9+6vh/neoW5XK4FuYNzFYar/ZCQycr810/6sei1VDtMDAaiaFSqgu4SiuM8QRB2lFAotCAp22azMTIysuCa8fFxQqEQLS0tnDlzZtPWlkgkmJ2dXZALNTExwcjICHv37sViKVwCtcViIRgMEovFClvWvgFzwZ3FYuFr7hVaFvh7IZOc7w1VU1NDJpNZcizOZlAUhUsjQd7umiCWynCk1sVez+JjUr/fj8/no7WkhIvDEm/eGOMbR4o7MDmTyTA6Okp1dfWCHLN0VuZglYPGUuvti2uPIQHW0dGNB9dXf0ZMZSND1apFEDajlqqqaqZHupmdnV3zEbMkSezfv39RAYJOp8NkMq36NWjUKp4+UIXTpCecTBelclIEUYIg7Hh35k3IssypU6d4+umnV/ycc+fOcf78eWZnZ+no6OCFF17ghRde2NA6fD7f/Jw8yFXQXbp0CavVSnNz84bufTez2UxFRcWSeSpbQZZlfD4fDodj/qEYiCbRqlVYDXcdBU12gs4Mjjogl3Df1NS0ySu+7RdXRrg8EqTaYeL5++rnd57u5Pf7+ed//mcSiQQWi4VDR77E2fEw3VMhdrkL2FbgLuPj42QymUU9xUpMOp4+sESfMTmL15RBctTm/6LZNAT6CVn3ozKq1hT8/+7jh3n7Td987t9aSJK07HH7cgO5ZVnh1I0xSs167qsrpeHzILLEpCtK2wkRRAmCsKPYbLYFRxWhUGg+IRVyoyOmpqb4+7//eyB3JPCTn/yEr3/96wv+we7o6KCjo4PXX3+dc+fObXhdiUSCUChEWVnZ/I6BLMuUl5fj9XpXrVRbrzuDte0gHA4zPT2N2WxGp9MRT2X4q/dvcay+lMd239X7x14F9mqQFh4RjYyM4Pf7OXDgQNHXG09lUKskdBo1Bysd1LvMHKh0LJvIPDQ0xOjoKAaDAb/fz969U7jMZZy6Pk5DqQV1kcaUDA0NYTQa5wc3zwnGkpQYdYvXO/IpUt+7cPQPkHXW/JLLZ4dByWIrr0FncK/pHnaLmePHjy8qqFiO3+9ncnKSXbt2rZhvdWdyeSKd5WcXB+n1RXigoWzZzykkkRMlCMKO4vV68fv9BINBMpkMnZ2dC5JSDQYD3//+9/ne977H9773PaqqqhYFUMUgyzJms3lBYKPVamlvby/aRPpsNsvMzAzp9AoJ3JskGAyi0+nmjxaNOg2NpRaujs0sPpapvh+q71t0j3A4THd37kioWBRF4eJwgP/3vS7e684VJNSXWjhY5Vw2gEqn0wwMDKBSqTCZTJhMJurq6nii1YM2FeZGV/eakqDzUVFRwe7duxesLZrMn3t4XgAAIABJREFU8JfvdPFx//TiTyjbDcBUz0V6e3vzO9abGQRJjbmiaV2B+pVAltc6x9Z0bVdXF729vcv+mSuKQk9Pz3zRSCCa5G/O9NDvj/Kb+6t4fM/mNOUUO1GCIOwoarWaEydO8PLLL6MoCu3t7bjdbk6fPo3X62X37t1bsq65ByvkHgBXrlyhqqqqqLtF2WyW0dFRKioqtnRXKpVKEY1GcbvdCx6K+7wl/N3ZXl65MMSxhtLccUuwH2xVoF68+9DQ0EBXVxe3bt3iyJEjBV/n+GycN66NMhKMUe0wsa9ybcdOgUAASZL45je/CUBZWdn8n3ekTM3Na1fpvnkdr9dLXV3doj+HjWhsbFz0vsFArvpsyeMrowPsVehm+kg5ykgkEst2jV9WcICIsRJNWsawjg3UrKzw2eVr7DWlaGle/ng2FAoxNjZGa2vrsoUfkiSh0WiIRCLYHC7+5kwPCvDb99VT59q85qwiiBIEYcdpbm5elGP0yCOPLHntt7/97aKvZ3Z2FovFMn9kNzo6Snd3NyaTqajBjU6nQ6/XE4lEtjSICgaDSJK0KBfGrFPzYe8U79yapNZp5gdPN+O5/hNmKr6AY/fDudL8O+j1eurr6+nt7aW1tbWgCfOfDfo5eW0Uk07D0weqVjy6u1t5eTlPPPHEoj5NAO3t7bgqKvn4yk0mJycZHh7G7Xbz0EMPbXjNY2NjlJaWLkq8HghEcv2h7IvXk1vwXqxd/4qUjhIKhdYXRCkKirOR8YCCbnKS2tq151a1lNs4FQrw8eVrNO9qXPbPt7u7G7VavWSAeCez2czU1BRaFTzaUkG9y4LTvLk5gOI4TxAEoYgSiQQjIyMEArmS+Ewmw+XLlykpKVn1IbGs2REYPJP77yosFgvRaHRLO34rioLNZluU2zI2m6CxzEp7tQOdRoW/7yKz8RQvdev4z/96jb8728tbN8a5MTFLIp1rh7Br1y4g96AtxLqSn9+3zmnmvloX/+GLLSse3d1pZGSEoaEhYHGjyzt1h2SuZ0o4+MCXuP/++2loaAByO4VnzpxhaGho3d3lI5EIZ86coa+vb9HHBvxRahzm5fOwynajVqkwZ2YIh8PrO9KTJJKe+0gZStc9mqjGYcZW6mZocnrZFguJRILBwUHq6upWLIqQZYWLE1FmYikikQiHa1ybHkCBCKIEQRCKaq4iby6h9saNG8Tjcdrb2/NL6p0Zhjf/FD57Cd77v1cNpKxW63z38q1SUVGxZM5ZncuMRa9FAewGLTWpfszuev5NRyuHqh1kZIWPB6b56flBgrFcTtF0QiFr95DWmkln8w8Mx2fj/OhsL69dzf35lVkNPLm3EoN2bedTwWCQc+fO0de3eu+jBxvKsOg1vHlzkqqqKqqqqgCIRqPMzs7y6aef8sYbb3D+/Hn8fv+agprh4WGARVV50WSGqXBi5V5IWhN0fBtr/WGSyeT68rVifkIzuZ3FOws21kKlktjX1EAglmLw8+DzbplMhoqKihWrMVOZLD+9MMiZwVlmktmNNw7dAHGcJwiCUCTxeJxQKITb7Z5vcNjd3U19fX3+x2t970CgD4xOUGlhZgisnlwzxSWYTCbUajWJRGLDQ43zkUql0OmWqBIjl7PzZyf2MeCP0mCI4OgJQ+Ux9nkd7KvMjQfJZGUmQgnc1twuQ+90mGsJM9cG4vyPoWuUWw1UlZj4yh4PGvXqQWk8leH0rUnODfkx6TR01DjXPD5kTiKR4MyZM+h0Oo4ePbrq5+q1ah7f7eHVy8NcHg1ysCoXUNtsNp544gmmp6cZGBhgeHiY/v5+Hn30URwOx7LrUhSF4eFhSktLFx1p6jQqvna4lnLr8jtjAJjLsOkzKLC+ytDO/044YcdUc19ejWoP1buZ7PEyPDzKvra2RV+fxWLh2LFjy37+bDzFP50bwBdJcqKtkiabasXqvWITQZQgCEKR3L0LZbFYaGtrW1ceyQLZVK4ySmcBvQ00OiipgQs/zu0ulLdCaQtobh9rSJJEU1PTpndmh1zVWk9PDxUVFcuWtlc7Pm9+ONgNknq+emyORq2iymGaf/tLzRUcqXUx6Atx+cYtUpKefn8E9ef5U290jhKMpagqMVHlMFFpNzIdTTLgj6JVS7zfM0U8neVIrYtHmivWvPM0J5vNcvbsWdLpNA8//PCKx3h32l9ZwmdDft6+OcGecjv6z19XkiTKysooKyvj4MGDjI+Pz+eOXbp0iXg8Tm1tLR6PZ37ncm4A76FDhxa9jlatYk+FfU1r0gx/hCubBtdja7qeVIRM1E/KWIt7nbtQcxpKrZw4up/R0VEymcyCAMjn82E0GpftOzUbT/HDj3rIygrPddTRWJbfGgpJBFGCIAhFMHck43K5UKvVKIqCSqXaWFPNobOg0sBX/hNkUrkAyuYFVyNMXoebb4DqTSjdBVVHwJY7QtuKAApyD3tFUdbWib3mGLj35ILBVVj0WiqMcGN2lI5aF7t2Nc/vaBi1agYTad7rmURRcg/ey6NBapxmUOBQjZPfvr9hyYaZazE+Po7f7+fo0aPrGu4sSRJPtno5PxRAXua4TqvVLhiybDAYGB0dZWxsDL1eT21tLXV1dfT09OD3+5cM4C4MB6hxmCi1rCG4S8XITl4j7DyA2Vqy+o5OcBCNpNCyZy+KJf/B1rW1dajt7gXfl7Isc+7cOYxGIw8//PCSn2czaDlQ6aC9ykHZHTttyWQSWZbXX2VYACInShAEoQgkSaKmpobS0lJSqRS/+tWvmJyczP+GchamboC7FSo7oPZYrimlpIL6L8L9vw/tz0PFfggOQCyXyE4qijIzxMjwMH6/vyBf21ooikIwGMRisSyqHluSJOWOKNfI5XLhdrvp7u5ekDT/SEsFf/BQM3/y+F5+5/4GGsusGHUa6pwWkKC9ypl3AAVQVVXFY489Np/XtB6VJSZ+c38VRt3agto9e/Zw4sQJjh07hsvloqenh4sXL3Lx4kXS6TS//OUvF/ydxlIZXrsywo2JNc7FK99LNpNltOfa2mbpzQyCRo/K5tlQc9grY0H+9mwvQ77bPcLGxsaIRqPzhQNzFEXh/Z5JgrEkkiTx5T2eBQEU5BL8lxoyvhlEECUIglBgdybrSpLEtWvXmJ2d3dgIFpUaOv5X2PX40h+XpFxQ1fwV+MJ/yAVbAJPXkC79I6mutwj1fgZRX/5rWIdoNEo6nV7bbk33W9Dz9rpfo6WlhXg8Pl8hdye9Vk19qYWn9lfhsRkZCEQ2NHx2amqKYDAIsK4dqKWMzsR468b4mhLIVSoVXq+XY8eOceLECex2O9lsloqKCrLZLD7f7b/Puf5Qa/4a7dXojBYMiSnC4fCql6f9g/RRQyyeWNv9l9FUZiUVmua//eI1QqEQiqLQ1dWF1WrF47ndJDOdlfnZxSFOd01yZXRm2fuZzWZisdiWVKCKIEoQBKHAJicn6e/vR1EUAoEAfX19NDU15f/wTYZyO1Fq3ZqOu1Cpbyeaew/Cnv8Ji8VMfHqQzKc/gnN/C/L6SurXa2ZmBo1Gs3oFVzYNE5dz+V7r5Ha7cTgc3Lp1a9kH6Fzy+u9+oYk/O7Evr/lp4XCYjz/+mEuXLm18cC8wFIjyUZ+P7qnVA5c7GQyG+UHDc/l2d457GfBH0KpVeO1r3GmTpFzPqMQ4sfDMym0WFIVw9aPEzdUbHlFk0Wup9ZQzHU0yMjLC9PQ0wWCQXbt2zed9heJp/u5sLzcmZvnyHg8PNbmXv5/FgizLxGKxDa0rHyInShAEoYDi8TjhcBi3250bIXLxIgaDgdbW1vxuqChw7dVcLtTB59b/+WodlLdhtTXi675JRB+nRBXN3Q+g710wluQSujVrS5JeC4/HQzKZXL2Nw+CH4O+DuuPrfg1JkmhpaaG/v59UKrVskvd88noeUqkUZ86cQZIk7rvvvoJ0Gr+vzsX54QCnbozRUGpZU1XhHJfLxTPPPIPP51vQGR1gIBClxmla35y+8jZssSi+rEw4HMbhcCx9nSQRyurQW10FGWrdVl3GzSsmevoHMRgMGI3G+Xyw6UiCH3/STzKT5euH62guX7mq1GQyoVKpiEQia8u/KyARRAmCIBTQnRV54+PjBINB7r///vzLsKeuQWgMdv+bDa3LYDCg0ZuImMsomcvnkbMw3Q2x6dyRmrMByttyierhiVz7hJKa3DHhOqnVakx6DSRmc7tN2dTnv9K5+0sqGP4E3v3PufdfeDmXCL/O16qsrMwrP2ktZFnm008/JRqNcvz48YJ1SFerVDzR6uUfPu3nk4FpHmhcfpdlKS6Xa1GLjGQ6y3QkSZtnnbud5lL0e0+g6+khHo8vG0RlRi8R80cord5AYcQddpfb0ZeU0TUwQGWFm/vuu90ywWbQUlVi5OFd5ZSvIX9tbmbhVvSLEkGUIAhCgcxNnq+oqECtVuP1ennooYcWHLmsSzad2ymylEP5vg2tTZKkxW0GVGo48u8gPA5T12HyGkzfygVS3W9CKgbZBBx8PrdbNRcEVd8Pemvu2pHPbr8/m0LJpBmpPIG91IMteBX631+8mAf+MHcsOXIOUlHwHAAlkwva1hlEze0MxWIxMplMQXthDQ4OMjExwaFDh/L/O1xGU5mVlnIb7/VMsb/SgdWwsV5Heq2a7z/eipzHaaMENJQZURuX2cVRFMLdZ1Eox2o9vKF1znFZ9Dzb0cjPX36XX/1qBofDwa77H+FYaz16rZqvHa5b1/08no0lu+dLBFGCIAgFEIvFuHnzJvF4HMh1Ci8pKcHtXt8uwwLDn0AyDHt+M5e/skFLBgKSlGuTYPNC4yO5AcCB/lzOlMEOI11w43UoqQak3GDg8rZcEAW540atGQxaUOuIZdWEInGsThlcTbmeVurcx27/9/PjoN2/kQvelGzueLGkZvH61kBRFN577z3MZnNBZtLNqa2tRavVFm2n6yt7PFwbn8W4zl5Vy9Fp8rxPOob60j9A7RdylZ53i02jzUYpKXevuS/WWlhUGSwWCy5XKT2TQXoudGG0lXC0vnTd91pTBWgRiCBKEAShAKLRKDqdDrvdzsTEBG+88QZf/vKX89/BUJRcSXlZS97BxdK3VUin00s/dCQVOBtzQY5Kk9slKt8LD/4ROOpz77szmCttzv26w8zoKOpwOLcjpFLldtGW46iFh/90Q8eGkNuNamxs5MqVKwQCgWUbe67VzMwMer0eo9FYtAAKwGnWc3yFhOn1+MXlYepdFg5ULZPTtBKdGZz1jA90o+ia8N49oic4iEWdxtLUVpBgfo7d4cQv67lxcxSdVsNX9zdxf13+g7IDgQDZbLbgu4YrEdV5giAIBWA2m5EkiWg0ysDAAFardfkk3bWQJDjwDWg5UbhFkpu5tlRLgAXsVfDFP4HD34JH/s9coKTWrvoAzWazhEIh7Hb72ucC2qtu97zagPr6enQ6Hbdu3drQfeLxOB999BGffPJJQSrx1uLWZIifnh/I+/ViqQyXRoKEEun8F1G+FyUdY9Y3vKjSMekfIK1zgGFjrR3ultaa+Iha+vTVhCoP0tZQtaHE/Xg8TiAQ2LS/NxBBlCAIQkGYTKb5Cjyz2cyRI0fy7xQeD0A6lgtaClgxN7e2NQ2dzSO4mZ2dRZblDfdRyodWq6WxsZHR0dG1NY5cwp0jXdrb2wtSibcWiUyWGxMhLo0E8/r8uf5Qtc4NJL6XNmPTZJFDkwsTtBWFqUCEftlT8OBkMBCjrrKCBzsOYLGXMODfWGK42Wwmk8mQSGysj9V6iCBKEAShQAKBAFeuXMHpdC5oGrguipIb33LxH3K/L7C5EvBIJFLwe2u1WhwOx5aM3wBobGxEo9EQCATW/bmKonDhwgUCgQBHjhzBbl/b/LlC2Octodph4u2uCRLp7Lo/f64/VGXJBv7c1TrMFQ2oE0FCs7Pz75YVhUjF/VjrDxU8qKxzmbHqtUxFEhtqhDqnmN/byxE5UYIgCAXg9/v56U9/yvT0NOl0Gr/fv6gMfU2mu2B2BJqfKGj+yRy9Xo9eryccDm84d+huVqt19eaaRWQwGDhx4kRe7ST6+/sZHBxk7969VN6dE1Rkc3P1XjrTw3vdk3yl1buuzx8IRKl2rLM/1FLraHoUq8VPOBJBURQkSSISiSDLMlb7Bo6mlzHXCHXAH6XOlX8vrzkajQaDwUA0Gt20vCixEyUIglAAPp8Pk8nEgQMHkCRpwTiONZMz0HsazGW5sv8isVgsBR+TEYlEVu54vUnmAqj1HulUVVXR1tbG7t27i7GsVXlLTLRXOflk0E8gmlzz58mygs2gZVdZAYJXnQW704XD4Zj/3gjffAd1aKhgPbLuVu0wc7zJveEAao7VakWSpE3LixI7UYIg7Djd3d2cOnUKWZY5dOgQx48v7IZ95swZLly4gEqlwmw289RTT204j6esrAy1Wk0wGFw0jmPNRj7LNac88G9zlXJF4nA4sNlsBTuekWWZ4eFhbDbbpu/iLKWrq4sbN25w4sSJVUvfY7EYer0enU63ZQHUnEdbKqh3mXGY1l6ur1JJPHekvmBrsKR9WIZPg/MbKGgJ+yexlm0s4XszbailSB5EECUIwo4iyzInT57k+eefx2az8dJLL9HS0rLgH1ePx8MLL7yATqfjs88+46233uKrX/3qhl53pXEcaxbzg2tXrp1AERVibMed5hLKN1SNWEAVFRVcvXqV3t5e9uzZs+x1qVSKDz74AKvVyrFjxzZxhUsz6zXsq8z9Gc4dp60mk5XXNTZmVRojSniK6PBVzI5yGvVBlMr1j+TZamv989socZwnCMKOMjo6itPpxOl0otFoaGtro6ura8E1c+XwkDvGybea624ul4vdu3fnF0BBrvnk3qcLspbVJBIJpqamCnLscWdfpe3Abrfj8Xjo6elZ9ohRlmU++eQTotEozc2FGWVSKJdGArz4YTeZ7OrHrX9zpofXr44U7sUt5cxqyxns7yPh60erUtCVFTeoL7SJiQn6+vo25bVEECUIwo4SCoUWjP6w2WwrBkkXLlygqalpM5a2vJg/9wtuDwYusng8js/nI5lce/7NUhKJBLFYDIfDsa2OfFpaWkgmkwwMDCz58atXrzI5OUl7ezulpevvkF1MVr2WiVCCjwemV7wunsowGU5gN25sZMwCkoS1soVMPExn9xDTGm/B22wUm1arJZFIrN7GowBEECUIwo633MP98uXLjI2N8cADDyz62Llz53jxxRe5ceMGHR0d/PCHPyzO4hQFbr0Jl/4xNxB4k8xV0YXD4Q3dJxaLoVKpNrUlwFqUlpZSWlpKf3//ot22gYEBuru7aWpqor5+++2yNJZZ2V1u4/2eKULx5RtoDgaiKArUOZeZeZenZEkjvpSOmXiWW9FcEcK9ZK7VwWYMJBY5UYIg7Ch37zyFQqEly+57e3v54IMP+Na3vrVkU8yOjg46Ojp4/fXXOXfuXPEW7O/JjXfZ9eXcQOBNotFoMBqNRCKRDZWDO51ObDZb/o1Fi+jw4cPo9fpFQbTT6aS+vp79+/dv0cpW95VWDz94/xZvd43zvxxceuzPQCCKVq3Cay/sMWp0NoAhHUSWdKgjUaJTA5jqWgv6GsWk0+nQarVEo9Gi5+mJnShBEHYUr9eL3+8nGAySyWTo7OykpaVlwTXj4+P88pe/5Otf//r8T61bQs5C76/AVAre9k1/eYvFQjwez7s1wdwOz3YMoCC326bT6VAUZX5moKIo2Gw2Dh8+vPbRNFvAYdLzhfoyro7N4I8sfeQ64I9QVWIqbGI5YE75MWjAaNSjJoM55S/o/YtNkiQsFguRz/tdFdP2/M4XBEHIk1qt5sSJE7z88ssoikJ7eztut5vTp0/j9XrZvXs3//qv/0oqleKVV14BconI3/jGNzZ/saPnIR6Efb9V1JYGy7FarQSDQVKpVF6B0NDQEDqdLv/u7JsgGo1y5swZdu/eza1bt3A6nbS3b37Amo/jjWW0uG24LIurKRVF4UitC5Ou8I9xU3k9rTd+QTSjxaxJYyp/tuCvUWx2ux29Xl/0Kj0RRAmCsOM0Nzcvqrh65JFH5n//O7/zO5u9pKVlU7mWBq7GLXl5g8FAc3NzXg+ZVCpFJBLZ9L4862U0GpmdneXkyZOYTKYVWx5sNzqNmiqHCYBkOotee/u4V5IkDtfkWQW6GnsVpoe/h2lmCEpqNjwceiuYzeaiNQi9kwiiBEEQtkrdg0WZj7dWc8HT3JHHeoKpYDCIJElbMmx4PYLBIIODg/h8PpxOZ8F7ZG2GTwam+aBnin//xRYMnwdSozMxzDoNJetozLku9qp7Mni609ww4mIe2W/fA2FBEISdKuaHYH/u91vcFiCRSNDT07OuCixFUZiZmcFiseQ1p24z+Xw+jEYj+/btw2Kx5DeOZ4vVOMxEUxne7Z6cf9/rV0f4lysF7A+1AwUCAYaGhshmi1f1KoIoQRCEzdbzNlx7FTIb69FUCFqtlnQ6TSQSWfPnhMNhMpnMtulQvpKysjI0Gg2hUAiNRrNpg2kLyWM3cqjayaeDfnzhxHx/qDpX8Y+r7mVmsxlFUYra6kAc5wmCIGwmfy8E+qDxUdBs/dGSWq3GZDIRiUQoLy9f0+cYjUbKy8u3trJxjQoyjmcbeKS5gmvjs5y6McaRGleuP5QIolZkMplQqVREo9EFDXgLSQRRgiAIm0XOQu9pMDqh8vBWr2ae1WplYmKCVCq16sBeyO1ebbcu3ytxuVz3bPA0x6zX8KXmct66OY5aktCoJSrtpq1e1rYmSRJms3ldu6zrJY7zBEEQNsv4JYhNQ+Mjm9pYczVzO0predjMzs4WbNagsD5Halz8+4daGAxEiaWyjIfiW72kbc9isZBKpYo2AkYEUYIgCJtFrYWy3eDa4ll9d9Hr9bhcrlUr1xRFYXJykmAwuEkrE+6kUkmEk2luTobonQ7zH09eZThY/NEm9zKbzcauXbvWtMOaDxFECYIgbJaK/bD3f97yirylVFRUrNpXJxqNkk6nt31bg51swB9FkmBPuZ2MLDPgF0HUSjQaTdECKBBBlCAIQvHFAjB2ERR5q1eyLEVRSCRWnnwfDAbRaDRLziIUNkedy4xGpWIgEEGjUonk8jWIxWL09fXh8/kKPkxZJJYLgiAUW+/p3JDh0l2g254VbYqi0N/fj91ux+v1Lvp4JpMhHA7jdDq39cy5na7aYebPTuxjwB+lzmWm2iGCqNVEIhEGBgYwm82o1WpaW1sxmQqTlC+CKEEQhGIK9oO/Gxoe3rYBFIBKpVowtPXu7uXpdBqdTndP9Iba6aodInhaD0mSsFqtmEwmYrEY0Wi0YEGU+HFCEAShWBQZen4FBjtUHdnq1azKYrGQTqdJJhc3ATUajTQ2Nt6TY1OEX29msxlJkojFYvNtDwpF7EQJgiAUy/hliPpyyeSq7f/P7Vyrg3A4jMFgmH9/JpNBpVKJYzzhnmQymWhtbSUajWI2mwu2CwViJ0oQBKF4jCW5irzSlq1eyZpotVqMRuOiMRmTk5P09vbODyoWhHuNyWSirKysoAEUiJ0oQRCE4nHU537dQyorK9Fobj8astksoVAIu92+KE9KEH7diZ0oQRCEQosHoe/dbTFgeL30ej1q9e1u6rOzs8iyLBLKBWEJIogSBEEotL53YOQzyBZn1ESxBQIBfD4fiqIQDAYxGo0YjcatXpYgbDsiiBIEQSikmSHwdUHNF0B/bzaljMfj+P1+EokEiURCdCgXhGWIIEoQBKFQZobh0xdByUL1/Vu9mrxZrVay2SyKotDQ0IDdbt/qJQnCtiQSywVB2HG6u7s5deoUsixz6NAhjh8/vuDjmUyGV199lbGxMUwmE88+++zGc35mR+DN70NwEBx1EJkEe9XG7rlFzGYzmUyGkZERqqqqFuRICYJwm9iJEgRhR5FlmZMnT/Lcc8/x3e9+l87OTqamphZcc+HCBQwGA3/4h3/I0aNHefvttzf+wjNDoNKDuxV01tzb96hkMonP52NwcJBr164VfN6YIOwUIogSBGFHGR0dxel04nQ60Wg0tLW10dXVteCarq4uDh48CEBrayt9fX0b74FUUgN6M6jUoNbk3r5HzY3FsFqtKIqyqG+UIAg54jhPEIQdJRQKYbPZ5t+22WyMjIwse41arcZgMBCLxRaMgzh37hznz59ndnaWjo4OXnjhBV544YXlX9heBV/8k9wOVEnNPXuUB7njPJ1ONz9Dr5BjMgRhJxFBlCAIO95amkTefU1HRwcdHR28/vrrnDt3bm0vZK+6p4OnOcUckyEIO4kIogRB2FFsNhuhUGj+7VAohNVqXfIau91ONpslkUiIPkh3MZlMIngShFWInChBEHYUr9eL3+8nGAySyWTo7OykpWXh7LqWlhYuXboEwPXr16mvrxcjTQRBWDexEyUIwo6iVqs5ceIEL7/8Moqi0N7ejtvt5vTp03i9Xnbv3k17ezuvvvoqf/mXf4nRaOTZZ5/d6mULgnAPEkGUIAg7TnNzM83NzQve98gjj8z/XqvV8lu/9VubvSxBEHYYEUQJgiCs4Pr163i93jVfH4vFdkwukfhatq+d9PVsx69lZmZmTdeJIEoQBGEF3//+99d1/Ysvvsh3vvOdIq1mc4mvZfvaSV/Pvfy1iMRyQRAEQRCEPIggShAEQRAEIQ/qhx9++M+3ehGCIAg7yXpyqLY78bVsXzvp67lXvxbpz//8zzc4MEoQBEEQBOHXjzjOEwRBEARByIOozhMEQSiA7u5uTp06hSzLHDp0iOPHj2/1kvI2OzvLq6++SiQSQZIkDh8+zNGjR7d6WRsiyzI//OEPsVqtPPfcc1u9nLzF43Fee+01pqamkCSJp556iurq6q1eVl7Onj3LhQsXACgvL+epp55Cq9Vu8arWRwRRgiAIGyTLMidPnuT555/HZrPx0ksv0dLSgtvt3uql5UWlUvHFf9TpAAADDklEQVTlL38Zr9dLMpnkxRdfpKGh4Z79egA+/vhjSktLSSaTW72UDTl16hRNTU187WtfI5PJkE6nt3pJeQmFQnzyySd897vfRavV8sorr9DZ2Ul7e/tWL21dxHGeIAjCBo2OjuJ0OnE6nWg0Gtra2ujq6trqZeXNarXOJ/rq9XrKysoIh8NbvKr8zc7O0t3dzaFDh7Z6KRuSSCQYHByc/zo0Gs09PThblmXS6TTZbJZ0Or1oUPi9QOxECYIgbFAoFMJms82/bbPZGBkZ2cIVFU4wGGR8fJzKysqtXkreTp06xeOPP37P70IFg0FMJhO/+MUvmJycxOPx8OSTT6LT6bZ6aetms9k4duwYf/EXf4FWq6WxsZGmpqatXta6iZ0oQRCEIpAkaauXsGHJZJJXXnmFJ554AoPBsNXLyUtXVxdms/meLaG/kyzLjI+Pc+TIEX7/938fnU7Hhx9+uNXLyks8HufmzZv80R/9EX/8x39MKpXi8uXLW72sdRM7UYIgCBtks9kIhULzb4dCoXvyaOJO2WyWV155hX379tHa2rrVy8nb8PAwXV1ddHd3k8lkSCaT/PznP+eZZ57Z6qWtm81mw2azUVVVBUBra+s9G0T19fXhcDgwm80A7Nmzh+HhYQ4cOLDFK1sfEUQJgiBskNfrxe/3EwwGsVqtdHZ23pMP6TmKovAv//IvlJaWcuzYsa1ezoY89thjPPbYYwD09/dz5syZe/bvxmq1YrfbmZ6eprS0lL6+PsrKyrZ6WXmx2+2MjIyQSqXQarX09/ffk7uFIogSBEHYILVazYkTJ3j55ZdRFIX29vZ7upJtaGiIK1eu4Ha7+eu//msAHn30UZqbm7d4ZcKTTz7Jz3/+c7LZLA6Hg6effnqrl5SXqqoqWltbefHFF1GpVHg8Hg4fPrzVy1o30bFcEARBEAQhDyKxXBAEQRAEIQ8iiBIEQRAEQciDCKIEQRAEQRDyIIIoQRAEQRCEPIggShAEQRAEIQ8iiBIEQRAEQciDCKIEQRAEQRDyIIIoQRAEQRCEPPz/juclsDi5/7wAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x432 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 子图创建1 --- 先建立子图然后填充图表\n",
    "\n",
    "fig = plt.figure(figsize=(10,6),facecolor = 'gray')\n",
    "\n",
    "ax1 = fig.add_subplot(2,2,1)            # 第一行的左图\n",
    "plt.plot(np.random.rand(50).cumsum(),'k--')\n",
    "plt.plot(np.random.randn(50).cumsum(),'b--')\n",
    "# 先创建图表figure，然后生成子图，（2,2,1）代表创建2*2的矩阵表格，然后选择第一个，顺序是从左到右，从上到下\n",
    "# 创建子图后绘制图表，会绘制到最后一个子图\n",
    "\n",
    "ax2 = fig.add_subplot(2,2,2)          # 第一行的右图\n",
    "ax2.hist(np.random.rand(50),alpha = 0.5)\n",
    "\n",
    "ax4 = fig.add_subplot(2,2,4)        # 第二行的右图\n",
    "df = pd.DataFrame(np.random.rand(10,4),columns = list('abcd'))\n",
    "ax4.plot(df,alpha = 0.5,linestyle = '--',marker = '.')\n",
    "# 也可以直接在子图后用图表创建函数直接生成图表"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[<matplotlib.axes._subplots.AxesSubplot object at 0x000001E7B16BA240>\n",
      "  <matplotlib.axes._subplots.AxesSubplot object at 0x000001E7B172CAC8>\n",
      "  <matplotlib.axes._subplots.AxesSubplot object at 0x000001E7B27AF198>]\n",
      " [<matplotlib.axes._subplots.AxesSubplot object at 0x000001E7B27D47B8>\n",
      "  <matplotlib.axes._subplots.AxesSubplot object at 0x000001E7B27FAE48>\n",
      "  <matplotlib.axes._subplots.AxesSubplot object at 0x000001E7B282D4E0>]] (2, 3) <class 'numpy.ndarray'>\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1e7b28601d0>]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 子图创建2 --- 创建一个新的figure，并返回一个subplot对象的numpy数组 ---> plt.subplot\n",
    "\n",
    "fig,axes = plt.subplots(2,3,figsize = (10,4))\n",
    "ts = pd.Series(np.random.randn(1000).cumsum())\n",
    "print(axes,axes.shape,type(axes))\n",
    "# 生成图表对象的数组\n",
    "\n",
    "ax1 = axes[0,1]\n",
    "ax1.plot(ts)\n",
    "axes[0,0].plot(np.random.rand(100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plt.subplots 参数调整\n",
    "\n",
    "fig,axes = plt.subplots(2,2,sharex=True,sharey=True)\n",
    "# sharex, sharey : 是否共享x和y的刻度\n",
    "\n",
    "for i in range(2):\n",
    "    for j in range(2):\n",
    "        axes[i,j].hist(np.random.randn(500),color = 'pink',alpha = 0.5)\n",
    "plt.subplots_adjust(wspace = 0, hspace = 0)\n",
    "# wspace,hspace : 用于控制宽度和高度的百分比，比如subplots之间的间距"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 子图创建3 --- 多系列图，分别绘制\n",
    "\n",
    "df = pd.DataFrame(np.random.randn(1000,4),index = ts.index,columns = list('ABCD'))\n",
    "df = df.cumsum()\n",
    "df.plot(style = '--.',alpha = 0.4,grid = True,figsize = (8,8),\n",
    "       subplots = True,\n",
    "       layout = (2,3),\n",
    "       sharex = False)\n",
    "plt.subplots_adjust(wspace = 0, hspace = 0.2)\n",
    "\n",
    "# plt.plot()基本图表绘制函数 ---> subplots，是否分别绘制系列子图\n",
    "# layout：绘制子图矩阵，按照顺序填充"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
